Tootfinder

Projection-based multifidelity linear regression for data-scarce applications
Vignesh Sella, Julie Pham, Karen Willcox, Anirban Chaudhuri
https://arxiv.org/abs/2508.08517 https:…

Projection-based multifidelity linear regression for data-scarce applications
Surrogate modeling for systems with high-dimensional quantities of interest remains challenging, particularly when training data are costly to acquire. This work develops multifidelity methods for multiple-input multiple-output linear regression targeting data-limited applications with high-dimensional outputs. Multifidelity methods integrate many inexpensive low-fidelity model evaluations with limited, costly high-fidelity evaluations. We introduce two projection-based multifidelity linear reg…

Estimating the Number of Components in Panel Data Finite Mixture Regression Models with an Application to Production Function Heterogeneity
Yu Hao, Hiroyuki Kasahara
https://arxiv.org/abs/2506.09666

Estimating the Number of Components in Panel Data Finite Mixture Regression Models with an Application to Production Function Heterogeneity
This paper develops statistical methods for determining the number of components in panel data finite mixture regression models with regression errors independently distributed as normal or more flexible normal mixtures. We analyze the asymptotic properties of the likelihood ratio test (LRT) and information criteria (AIC and BIC) for model selection in both conditionally independent and dynamic panel settings. Unlike cross-sectional normal mixture models, we show that panel data structures elim…

Soil Texture Prediction with Bayesian Generalized Additive Models for Spatial Compositional Data
Joaqu\'in Mart\'inez-Minaya, Lore Zumeta-Olaskoaga, Dae-Jin Lee
https://arxiv.org/abs/2508.07708

Soil Texture Prediction with Bayesian Generalized Additive Models for Spatial Compositional Data
Compositional data (CoDa) plays an important role in many fields such as ecology, geology, or biology. The most widely used modeling approaches are based on the Dirichlet and the logistic-normal formulation under Aitchison geometry. Recent developments in the mathematical field on the simplex geometry allow to express the regression model in terms of coordinates and estimate its coefficients. Once the model is projected in the real space, we can employ a multivariate Gaussian regression to deal…

Multi-Task Reward Learning from Human Ratings
Mingkang Wu, Devin White, Evelyn Rose, Vernon Lawhern, Nicholas R Waytowich, Yongcan Cao
https://arxiv.org/abs/2506.09183

Multi-Task Reward Learning from Human Ratings
Reinforcement learning from human feeback (RLHF) has become a key factor in aligning model behavior with users' goals. However, while humans integrate multiple strategies when making decisions, current RLHF approaches often simplify this process by modeling human reasoning through isolated tasks such as classification or regression. In this paper, we propose a novel reinforcement learning (RL) method that mimics human decision-making by jointly considering multiple tasks. Specifically, we lever…

Efficient data-driven regression for reduced-order modeling of spatial pattern formation
Alessandro Alla, Rudy Geelen, Hannah Lu
https://arxiv.org/abs/2508.06833 https://…

Efficient data-driven regression for reduced-order modeling of spatial pattern formation
We present an efficient data-driven regression approach for constructing reduced-order models (ROMs) of reaction-diffusion systems exhibiting pattern formation. The ROMs are learned non-intrusively from available training data of physically accurate numerical simulations. The method can be applied to general nonlinear systems through the use of polynomial model form, while not requiring knowledge of the underlying physical model, governing equations, or numerical solvers. The process of learnin…

Data-Driven Nonlinear Regulation: Gaussian Process Learning
Telema Harry, Martin Guay, Shimin Wang, Richard D. Braatz
https://arxiv.org/abs/2506.09273 http…

Data-Driven Nonlinear Regulation: Gaussian Process Learning
This article addresses the output regulation problem for a class of nonlinear systems using a data-driven approach. An output feedback controller is proposed that integrates a traditional control component with a data-driven learning algorithm based on Gaussian Process (GP) regression to learn the nonlinear internal model. Specifically, a data-driven technique is employed to directly approximate the unknown internal model steady-state map from observed input-output data online. Our method does …

An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia
Wishnu Badrawani
https://arxiv.org/abs/2506.10369

An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia
This paper evaluates the performance of prominent machine learning (ML) algorithms in predicting Indonesia's inflation using the payment system, capital market, and macroeconomic data. We compare the forecasting performance of each ML model, namely shrinkage regression, ensemble learning, and super vector regression, to that of the univariate time series ARIMA and SARIMA models. We examine various out-of-bag sample periods in each ML model to determine the appropriate data-splitting ratios for …

Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
Marcos Matabuena, Rahul Ghosal, Pavlo Mozharovskyi, Oscar Hernan Madrid Padilla, Jukka-Pekka Onnela
https://arxiv.org/abs/2506.08325

Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
Depth measures are powerful tools for defining level sets in emerging, non--standard, and complex random objects such as high-dimensional multivariate data, functional data, and random graphs. Despite their favorable theoretical properties, the integration of depth measures into regression modeling to provide prediction regions remains a largely underexplored area of research. To address this gap, we propose a novel, model-free uncertainty quantification algorithm based on conditional depth mea…

Akaike information criterion for segmented regression models
Kazuki Nakajima, Yoshiyuki Ninomiya
https://arxiv.org/abs/2506.08760 https://

Akaike information criterion for segmented regression models
In segmented regression, when the regression function is continuous at the change-points that are the boundaries of the segments, it is also called joinpoint regression, and the analysis package developed by \cite{KimFFM00} has become a standard tool for analyzing trends in longitudinal data in the field of epidemiology. In addition, it is sometimes natural to expect the regression function to be discontinuous at the change-points, and in the field of epidemiology, this model is used in \cite{J…

Feature-free regression kriging
Peng Luo, Yilong Wu, Yongze Song
https://arxiv.org/abs/2507.07382 https://arxiv.org/pdf/2507.07382

Feature-free regression kriging
Spatial interpolation is a crucial task in geography. As perhaps the most widely used interpolation methods, geostatistical models -- such as Ordinary Kriging (OK) -- assume spatial stationarity, which makes it difficult to capture the nonstationary characteristics of geographic variables. A common solution is trend surface modeling (e.g., Regression Kriging, RK), which relies on external explanatory variables to model the trend and then applies geostatistical interpolation to the residuals. Ho…

Diffusion index forecasts under weaker loadings: PCA, ridge regression, and random projections
Tom Boot, Bart Keijsers
https://arxiv.org/abs/2506.09575 htt…

Diffusion index forecasts under weaker loadings: PCA, ridge regression, and random projections
We study the accuracy of forecasts in the diffusion index forecast model with possibly weak loadings. The default option to construct forecasts is to estimate the factors through principal component analysis (PCA) on the available predictor matrix, and use the estimated factors to forecast the outcome variable. Alternatively, we can directly relate the outcome variable to the predictors through either ridge regression or random projections. We establish that forecasts based on PCA, ridge regres…

UPLME: Uncertainty-Aware Probabilistic Language Modelling for Robust Empathy Regression
Md Rakibul Hasan, Md Zakir Hossain, Aneesh Krishna, Shafin Rahman, Tom Gedeon
https://arxiv.org/abs/2508.03520

UPLME: Uncertainty-Aware Probabilistic Language Modelling for Robust Empathy Regression
Supervised learning for empathy regression is challenged by noisy self-reported empathy scores. While many algorithms have been proposed for learning with noisy labels in textual classification problems, the regression counterpart is relatively under-explored. We propose UPLME, an uncertainty-aware probabilistic language modelling framework to capture label noise in the regression setting of empathy detection. UPLME includes a probabilistic language model that predicts both empathy score and he…

A symbolic regression-based implicit algebraic stress turbulence model: incorporating the production of non-dimensional Reynolds stress deviatoric tensor
Ziqi Ji, Penghao Duan, Gang Du
https://arxiv.org/abs/2507.04679

A symbolic regression-based implicit algebraic stress turbulence model: incorporating the production of non-dimensional Reynolds stress deviatoric tensor
Turbulence constitutes an exceptionally complex and irregular flow phenomenon that manifests in liquids, gases, and plasma, making it ubiquitous in both natural processes and engineering applications. Given the relatively modest advancements in classical turbulence models over the past half-century, data-driven approaches, such as machine learning, have recently gained considerable traction in turbulence model research. In this study, we introduce a symbolic regression-based implicit algebraic …

This https://arxiv.org/abs/2312.01602 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum Time Series Similarity Measures and Quantum Temporal Kernels
This article presents a quantum computing approach to designing of similarity measures and kernels for classification of stochastic symbolic time series. In the area of machine learning, kernels are important components of various similarity-based classification, clustering, and regression algorithms. An effective strategy for devising problem-specific kernels is leveraging existing generative models of the example space. In this study we assume that a quantum generative model, known as quantum…

A Framework for Creating Non-Regressive Test Cases via Branch Consistency Analysis Driven by Descriptions
Yuxiang Zhang, Pengyu Xue, Zhen Yang, Xiaoxue Ren, Xiang Li, Linhao Wu, Jiancheng Zhao, Xingda Yu
https://arxiv.org/abs/2506.07486

A Framework for Creating Non-Regressive Test Cases via Branch Consistency Analysis Driven by Descriptions
Automated test-generation research overwhelmingly assumes the correctness of focal methods, yet practitioners routinely face non-regression scenarios where the focal method may be defective. A baseline evaluation of EvoSuite and two leading Large Language Model (LLM)-based generators, namely ChatTester and ChatUniTest, on defective focal methods reveals that despite achieving up to 83% of branch coverage, none of the generated tests expose defects. To resolve this problem, we first construct …

Average quantile regression: a new non-mean regression model and coherent risk measure
Rong Jiang, M. C. Jones, Keming Yu, Jiangfeng Wang
https://arxiv.org/abs/2506.23059

Average quantile regression: a new non-mean regression model and coherent risk measure
Regression models that go beyond the mean, alongside coherent risk measures, have been important tools in modern data analysis. This paper introduces the innovative concept of Average Quantile Regression (AQR), which is smooth at the quantile-like level, comonotonically additive, and explicitly accounts for the severity of tail losses relative to quantile regression. AQR serves as a versatile regression model capable of describing distributional information across all positions, akin to quantil…

Identifying Optimal Regression Models For DEM Simulation Datasets
B. D. Jenkins, A. L. Nicusan, A. Neveu, G. Lumay, F. Francqui, J. P. K. Seville, C. R. K. Windows-Yule
https://arxiv.org/abs/2508.05308

Identifying Optimal Regression Models For DEM Simulation Datasets
Developing fast regression models (surrogate/metamodels) from DEM data is key for practical industrial application to allow real-time evaluations. However, benchmarking different models is often overlooked in particle technology for regression tasks, as model selection is frequently not the primary research focus. This can lead to the use of suboptimal models, resulting in subpar predictive accuracy, slow evaluations, or poor generalisation, hindering effective real-time decision-making and pro…

This https://arxiv.org/abs/2506.01348 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Distributionally Robust Learning in Survival Analysis
We introduce an innovative approach that incorporates a Distributionally Robust Learning (DRL) approach into Cox regression to enhance the robustness and accuracy of survival predictions. By formulating a DRL framework with a Wasserstein distance-based ambiguity set, we develop a variant Cox model that is less sensitive to assumptions about the underlying data distribution and more resilient to model misspecification and data perturbations. By leveraging Wasserstein duality, we reformulate the …

Primer to get you started with Optimization and Mathematical Programming in R #rstats

Galerkin-ARIMA: A Two-Stage Polynomial Regression Framework for Fast Rolling One-Step-Ahead Forecasting
Haojie Liu, Zihan Lin
https://arxiv.org/abs/2507.07469

Galerkin-ARIMA: A Two-Stage Polynomial Regression Framework for Fast Rolling One-Step-Ahead Forecasting
Time-series models like ARIMA remain widely used for forecasting but limited to linear assumptions and high computational cost in large and complex datasets. We propose Galerkin-ARIMA that generalizes the AR component of ARIMA and replace it with a flexible spline-based function estimated by Galerkin projection. This enables the model to capture nonlinear dependencies in lagged values and retain the MA component and Gaussian noise assumption. We derive a closed-form OLS estimator for the Galerk…

Constraint-Guided Symbolic Regression for Data-Efficient Kinetic Model Discovery
Miguel \'Angel de Carvalho Servia (Mimi), Ilya Orson Sandoval (Mimi), King Kuok (Mimi), Hii, Klaus Hellgardt, Dongda Zhang, Ehecatl Antonio del Rio Chanona
https://arxiv.org/abs/2507.02730

Constraint-Guided Symbolic Regression for Data-Efficient Kinetic Model Discovery
The industrialization of catalytic processes hinges on the availability of reliable kinetic models for design, optimization, and control. Traditional mechanistic models demand extensive domain expertise, while many data-driven approaches often lack interpretability and fail to enforce physical consistency. To overcome these limitations, we propose the Physics-Informed Automated Discovery of Kinetics (PI-ADoK) framework. By integrating physical constraints directly into a symbolic regression app…

ICAS: Detecting Training Data from Autoregressive Image Generative Models
Hongyao Yu, Yixiang Qiu, Yiheng Yang, Hao Fang, Tianqu Zhuang, Jiaxin Hong, Bin Chen, Hao Wu, Shu-Tao Xia
https://arxiv.org/abs/2507.05068

ICAS: Detecting Training Data from Autoregressive Image Generative Models
Autoregressive image generation has witnessed rapid advancements, with prominent models such as scale-wise visual auto-regression pushing the boundaries of visual synthesis. However, these developments also raise significant concerns regarding data privacy and copyright. In response, training data detection has emerged as a critical task for identifying unauthorized data usage in model training. To better understand the vulnerability of autoregressive image generative models to such detection, …

Discovering the underlying analytic structure within Standard Model constants using artificial intelligence
S. V. Chekanov, H. Kjellerstrand
https://arxiv.org/abs/2507.00225

Discovering the underlying analytic structure within Standard Model constants using artificial intelligence
This paper presents a search for underlying analytic structures among the fundamental parameters of the Standard Model (SM) using symbolic regression and genetic programming. We identify the simplest analytic relationships connecting pairs of these constants and report several notable observations based on about a thousand expressions with relative precision better than 1%. These results may serve as valuable inputs for model builders and artificial intelligence methods aimed at uncovering hidd…

This https://arxiv.org/abs/2502.05210 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Regression and Forecasting of U.S. Stock Returns Based on LSTM
This paper analyses the investment returns of three stock sectors, Manuf, Hitec, and Other, in the U.S. stock market, based on the Fama-French three-factor model, the Carhart four-factor model, and the Fama-French five-factor model, in order to test the validity of the Fama-French three-factor model, the Carhart four-factor model, and the Fama-French five-factor model for the three sectors of the market. French five-factor model for the three sectors of the market. Also, the LSTM model is used …

Generative Flexible Latent Structure Regression (GFLSR) model
Clara Grazian, Qian Jin, Pierre Lafaye De Micheaux
https://arxiv.org/abs/2508.04393 https://a…

Generative Flexible Latent Structure Regression (GFLSR) model
Latent structure methods, specifically linear continuous latent structure methods, are a type of fundamental statistical learning strategy. They are widely used for dimension reduction, regression and prediction, in the fields of chemometrics, economics, social science and etc. However, due to the lack of model inference, generative form, and unidentifiable parameters, most of these methods are always used as an algorithm, instead of a model. This paper proposed a Generative Flexible Latent Str…

Residual 1D CNN for Low SFR Surface Density Regression: A Design Note
Po-Chieh Yu
#toXiv_bot_toot

This https://arxiv.org/abs/2503.00772 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Bayesian inference for dynamic spatial quantile models with interactive effects
With the rapid advancement of information technology and data collection systems, large-scale spatial panel data presents new methodological and computational challenges. This paper introduces a dynamic spatial panel quantile model that incorporates unobserved heterogeneity. The proposed model captures the dynamic structure of panel data, high-dimensional cross-sectional dependence, and allows for heterogeneous regression coefficients. To estimate the model, we propose a novel Bayesian Markov C…

ASP-Assisted Symbolic Regression: Uncovering Hidden Physics in Fluid Mechanics
Theofanis Aravanis, Grigorios Chrimatopoulos, Mohammad Ferdows, Michalis Xenos, Efstratios Em Tzirtzilakis
https://arxiv.org/abs/2507.17777

ASP-Assisted Symbolic Regression: Uncovering Hidden Physics in Fluid Mechanics
Unlike conventional Machine-Learning (ML) approaches, often criticized as "black boxes", Symbolic Regression (SR) stands out as a powerful tool for revealing interpretable mathematical relationships in complex physical systems, requiring no a priori assumptions about models' structures. Motivated by the recognition that, in fluid mechanics, an understanding of the underlying flow physics is as crucial as accurate prediction, this study applies SR to model a fundamental three-dimensional (3D) in…

A Self-scaled Approximate $\ell_0$ Regularization Robust Model for Outlier Detection
Pengyang Song, Jue Wang
https://arxiv.org/abs/2506.22277 https://

A Self-scaled Approximate $\ell_0$ Regularization Robust Model for Outlier Detection
Robust regression models in the presence of outliers have significant practical relevance in areas such as signal processing, financial econometrics, and energy management. Many existing robust regression methods, either grounded in statistical theory or sparse signal recovery, typically rely on the explicit or implicit assumption of outlier sparsity to filter anomalies and recover the underlying signal or data. However, these methods often suffer from limited robustness or high computational c…

Large scale study of primary school student performance relative to their LMS activity and socioeconomic demographics using a Bayesian Additive Regression Trees containing random effects
Natalia da Silva, Bruno Tancredi, Ignacio Alvarez-Castro
https://arxiv.org/abs/2507.05262

Large scale study of primary school student performance relative to their LMS activity and socioeconomic demographics using a Bayesian Additive Regression Trees containing random effects
Using data collected on almost every 9-12 years old student in Uruguay, we show how to apply Bayesian Additive Regression Trees (BART) with random effects to study performance association with Learning Managment System (LMS) activity and socioeconomic status. Performance data is joined with LMS activity pattern data. BART is chosen because it is possible to include school-level random effects. The model can be used for early identification of at-risk students, and highlights schools that are su…

Modeling the Effective Elastic Modulus and Thickness of Corrugated Boards Using Gaussian Process Regression and Expected Hypervolume Improvement
Ricardo Fitas
https://arxiv.org/abs/2507.02208

Modeling the Effective Elastic Modulus and Thickness of Corrugated Boards Using Gaussian Process Regression and Expected Hypervolume Improvement
This work aims to model the hypersurface of the effective elastic modulus, \( E_{z, \text{eff}} \), and thickness, \( th_{\text{eff}} \), in corrugated boards. A Latin Hypercube Sampling (LHS) is followed by Gaussian Process Regression (GP), enhanced by EHVI as a multi-objective acquisition function. Accurate modeling of \( E_{z, \text{eff}} \) and \( th_{\text{eff}} \) is critical for optimizing the mechanical properties of corrugated materials in engineering applications. LHS provides an effi…

Generative Regression with IQ-BART
Sean O'Hagan, Veronika Ro\v{c}kov\'a
https://arxiv.org/abs/2507.04168 https://arxiv.org/pd…

Generative Regression with IQ-BART
Implicit Quantile BART (IQ-BART) posits a non-parametric Bayesian model on the conditional quantile function, acting as a model over a conditional model for $Y$ given $X$. One of the key ingredients is augmenting the observed data $\{(Y_i,X_i)\}_{i=1}^n$ with uniformly sampled values $τ_i$ for $1\leq i\leq n$ which serve as training data for quantile function estimation. Using the fact that the location parameter $μ$ in a $τ$-tilted asymmetric Laplace distribution corresponds to the $τ^{th}…

Nonlinear projection-based model order reduction with machine learning regression for closure error modeling in the latent space
S. Ares de Parga, Radek Tezaur, Carlos G. Hern\'andez, Charbel Farhat
https://arxiv.org/abs/2507.00634

Nonlinear projection-based model order reduction with machine learning regression for closure error modeling in the latent space
A significant advancement in nonlinear projection-based model order reduction (PMOR) is presented through a highly effective methodology. This methodology employs Gaussian process regression (GPR) and radial basis function (RBF) interpolation for closure error modeling in the latent space, offering notable gains in efficiency and expanding the scope of PMOR. Moving beyond the limitations of deep artificial neural networks (ANNs), previously used for this task, this approach provides crucial adv…

Integrating Pharmacokinetics and Pharmacodynamics Modeling with Quantum Regression for Predicting Herbal Compound Toxicity
Don Roosan, Saif Nirzhor, Rubayat Khan
https://arxiv.org/abs/2506.20157

Integrating Pharmacokinetics and Pharmacodynamics Modeling with Quantum Regression for Predicting Herbal Compound Toxicity
Herbal compounds present complex toxicity profiles that are often influenced by both intrinsic chemical properties and pharmacokinetics (PK) governing absorption and clearance. In this study, we develop a quantum regression model to predict acute toxicity severity for herbal-derived compounds by integrating toxicity data from NICEATM with pharmacological features from TCMSP.

ReCatcher: Towards LLMs Regression Testing for Code Generation
Altaf Allah Abbassi, Leuson Da Silva, Amin Nikanjam, Foutse Khomh
https://arxiv.org/abs/2507.19390 https://…

ReCatcher: Towards LLMs Regression Testing for Code Generation
Large Language Models (LLMs) for code generation evolve rapidly through fine-tuning, merging, or new model releases. However, such updates can introduce regressions, not only in correctness but also in code quality and performance. To address this, we present ReCatcher, a regression testing framework for Python code generation. ReCatcher systematically compares two LLMs, typically a current model and a candidate update, across three dimensions: logical correctness, static code quality, and exec…

Guidelines for LASSO and derivatives use under different dependence and scale structures
Laura Freijeiro-Gonz\'alez, Manuel Febrero-Bande, Wenceslao Gonz\'alez-Manteiga
https://arxiv.org/abs/2506.08582

Guidelines for LASSO and derivatives use under different dependence and scale structures
In a multivariate linear regression model with $p>1$ covariates, implementation of penalization techniques often implies a preliminary univariate standardization step. Although this prevents scale effects on the covariates selection procedure, possible dependence structures can be disrupted, leading to wrong results. This is particularly challenging in high-dimensional settings where $p \geq n$. In this paper, we analyze the standardization effect on the LASSO for different dependence-scales co…

Derivation of Tissue Properties from Basis-Vector Model Weights for Dual-Energy CT-Based Monte Carlo Proton Beam Dose Calculations
Maria Jose Medrano, Xinyuan Chen, Lucas Norberto Burigo, Joseph A. O'Sullivan, Jeffrey F. Williamson
https://arxiv.org/abs/2506.22425

Derivation of Tissue Properties from Basis-Vector Model Weights for Dual-Energy CT-Based Monte Carlo Proton Beam Dose Calculations
We propose a novel method, basis vector model material indexing (BVM-MI), for predicting atomic composition and mass density from two independent basis vector model weights derived from dual-energy CT (DECT) for Monte Carlo (MC) dose planning. BVM-MI employs multiple linear regression on BVM weights and their quotient to predict elemental composition and mass density for 70 representative tissues. Predicted values were imported into the TOPAS MC code to simulate proton dose deposition to a unif…

Use multi level models with {parsnip}: http://multilevelmod.tidymodels.org/ #rstats #ML

YOLO-Count: Differentiable Object Counting for Text-to-Image Generation
Guanning Zeng, Xiang Zhang, Zirui Wang, Haiyang Xu, Zeyuan Chen, Bingnan Li, Zhuowen Tu
https://arxiv.org/abs/2508.00728

YOLO-Count: Differentiable Object Counting for Text-to-Image Generation
We propose YOLO-Count, a differentiable open-vocabulary object counting model that tackles both general counting challenges and enables precise quantity control for text-to-image (T2I) generation. A core contribution is the 'cardinality' map, a novel regression target that accounts for variations in object size and spatial distribution. Leveraging representation alignment and a hybrid strong-weak supervision scheme, YOLO-Count bridges the gap between open-vocabulary counting and T2I generation …

Estimating properties of a homogeneous bounded soil using machine learning models
Konstantinos Kalimeris, Leonidas Mindrinos, Nikolaos Pallikarakis
https://arxiv.org/abs/2506.04256

Estimating properties of a homogeneous bounded soil using machine learning models
This work focuses on estimating soil properties from water moisture measurements. We consider simulated data generated by solving the initial-boundary value problem governing vertical infiltration in a homogeneous, bounded soil profile, with the usage of the Fokas method. To address the parameter identification problem, which is formulated as a two-output regression task, we explore various machine learning models. The performance of each model is assessed under different data conditions: full,…

This https://arxiv.org/abs/2502.14479 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework
The lifetime behaviour of loans is notoriously difficult to model, which can compromise a bank's financial reserves against future losses, if modelled poorly. Therefore, we present a data-driven comparative study amongst three techniques in modelling a series of default risk estimates over the lifetime of each loan, i.e., its term-structure. The behaviour of loans can be described using a nonstationary and time-dependent semi-Markov model, though we model its elements using a multistate regress…

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective
Roger Guimera, Marta Sales-Pardo
https://arxiv.org/abs/2507.19540 https://

Bayesian symbolic regression: Automated equation discovery from a physicists' perspective
Symbolic regression automates the process of learning closed-form mathematical models from data. Standard approaches to symbolic regression, as well as newer deep learning approaches, rely on heuristic model selection criteria, heuristic regularization, and heuristic exploration of model space. Here, we discuss the probabilistic approach to symbolic regression, an alternative to such heuristic approaches with direct connections to information theory and statistical physics. We show how the prob…

Modeling the Optical Properties of Biological Structures using Symbolic Regression
Julian Sierra-Velez, Alexandre Vial, Marina Inchaussandague, Diana Skigin, Demetrio Mac\'ias
https://arxiv.org/abs/2506.01862

Modeling the Optical Properties of Biological Structures using Symbolic Regression
We present a Machine Learning approach based on Symbolic Regression to derive, from either numerically generated or experimentally measured spectral data, closed-form expressions that model the optical properties of biological materials. To evaluate the performance of our approach, we consider three case studies with the aim of retrieving the refractive index of the materials that constitute the biological structures considered. The results obtained show that, in addition to retrieving readable…

Verification of the NOAA Space Weather Prediction Center solar flare forecast (1998-2024)
Enrico Camporeale, Thomas E. Berger
https://arxiv.org/abs/2508.01114 https://

Verification of the NOAA Space Weather Prediction Center solar flare forecast (1998-2024)
The NOAA Space Weather Prediction Center (SWPC) issues the official U.S. government forecast for M-class and X-class solar flares, yet the skill of these forecasts has never been comprehensively verified. In this study, we evaluate the SWPC probabilistic flare forecasts over a 26-year period (1998-2024), comparing them to several zero-cost and statistical baselines including persistence, climatology, Naive Bayes, and logistic regression. We find that the SWPC model does not outperform these bas…

Reproducing kernel Hilbert space methods for modelling the discount curve
Andreas Celary, Paul Kr\"uhner, Zehra Eksi
https://arxiv.org/abs/2506.03342 …

Reproducing kernel Hilbert space methods for modelling the discount curve
We consider the theory of bond discounts, defined as the difference between the terminal payoff of the contract and its current price. Working in the setting of finite-dimensional realizations in the HJM framework, under suitable notions of no-arbitrage, the admissible discount curves take the form of polynomial, exponential functions. We introduce reproducing kernels that are admissible under no-arbitrage as a tractable regression basis for the estimation problem in calibrating the model to ma…

Bayesian variable selection in a Cox proportional hazards model with the "Sum of Single Effects" prior
Yunqi Yang, Karl Tayeb, Peter Carbonetto, Xiaoyuan Zhong, Carole Ober, Matthew Stephens
https://arxiv.org/abs/2506.06233

Bayesian variable selection in a Cox proportional hazards model with the "Sum of Single Effects" prior
Motivated by genetic fine-mapping applications, we introduce a new approach to Bayesian variable selection regression (BVSR) for time-to-event (TTE) outcomes. This new approach is designed to deal with the specific challenges that arise in genetic fine-mapping, including: the presence of very strong correlations among the covariates, often exceeding 0.99; very large data sets containing potentially thousands of covariates and hundreds of thousands of samples. We accomplish this by extending the…

A Robust Controller based on Gaussian Processes for Robotic Manipulators with Unknown Uncertainty
Giulio Giacomuzzo, Mohamed Abdelwahab, Marco Cal\`i, Alberto Dalla Libera, Ruggero Carli
https://arxiv.org/abs/2507.11170

A Robust Controller based on Gaussian Processes for Robotic Manipulators with Unknown Uncertainty
In this paper, we propose a novel learning-based robust feedback linearization strategy to ensure precise trajectory tracking for an important family of Lagrangian systems. We assume a nominal knowledge of the dynamics is given but no a-priori bounds on the model mismatch are available. In our approach, the key ingredient is the adoption of a regression framework based on Gaussian Processes (GPR) to estimate the model mismatch. This estimate is added to the outer loop of a classical feedback li…

CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis
Jianfei Li, Kevin Kam Fung Yuen
https://arxiv.org/abs/2507.14022

CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis
This study proposes the Cognitive Pairwise Comparison Classification Model Selection (CPC-CMS) framework for document-level sentiment analysis. The CPC, based on expert knowledge judgment, is used to calculate the weights of evaluation criteria, including accuracy, precision, recall, F1-score, specificity, Matthews Correlation Coefficient (MCC), Cohen's Kappa (Kappa), and efficiency. Naive Bayes, Linear Support Vector Classification (LSVC), Random Forest, Logistic Regression, Extreme Gradient B…

FedCVD : Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization
Abdelrhman Gaber, Hassan Abd-Eltawab, John Elgallab, Youssif Abuzied, Dineo Mpanya, Turgay Celik, Swarun Kumar, Tamer ElBatt
https://arxiv.org/abs/2507.22963…

FedCVD++: Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization
Cardiovascular diseases (CVD) cause over 17 million deaths annually worldwide, highlighting the urgent need for privacy-preserving predictive systems. We introduce FedCVD++, an enhanced federated learning (FL) framework that integrates both parametric models (logistic regression, SVM, neural networks) and non-parametric models (Random Forest, XGBoost) for coronary heart disease risk prediction. To address key FL challenges, we propose: (1) tree-subset sampling that reduces Random Forest communi…

Fully Few-shot Class-incremental Audio Classification Using Multi-level Embedding Extractor and Ridge Regression Classifier
Yongjie Si, Yanxiong Li, Jiaxin Tan, Qianhua He, Il-Youp Kwak
https://arxiv.org/abs/2506.18406

Fully Few-shot Class-incremental Audio Classification Using Multi-level Embedding Extractor and Ridge Regression Classifier
In the task of Few-shot Class-incremental Audio Classification (FCAC), training samples of each base class are required to be abundant to train model. However, it is not easy to collect abundant training samples for many base classes due to data scarcity and high collection cost. We discuss a more realistic issue, Fully FCAC (FFCAC), in which training samples of both base and incremental classes are only a few. Furthermore, we propose a FFCAC method using a model which is decoupled into a multi…

FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment
Lee Qi Zun, Oscar Wong Jin Hao, Nor Anita Binti Che Omar, Zalifa Zakiah Binti Asnir, Mohamad Sabri bin Sinal Zainal, Goh Man Fye
https://arxiv.org/abs/2506.20303

FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment
Automated fundus image quality assessment (FIQA) remains a challenge due to variations in image acquisition and subjective expert evaluations. We introduce FundaQ-8, a novel expert-validated framework for systematically assessing fundus image quality using eight critical parameters, including field coverage, anatomical visibility, illumination, and image artifacts. Using FundaQ-8 as a structured scoring reference, we develop a ResNet18-based regression model to predict continuous quality scores…

(Exhaustive) Symbolic Regression and model selection by minimum description length
Harry Desmond
https://arxiv.org/abs/2507.13033 https://

(Exhaustive) Symbolic Regression and model selection by minimum description length
Symbolic regression is the machine learning method for learning functions from data. After a brief overview of the symbolic regression landscape, I will describe the two main challenges that traditional algorithms face: they have an unknown (and likely significant) probability of failing to find any given good function, and they suffer from ambiguity and poorly-justified assumptions in their function-selection procedure. To address these I propose an exhaustive search and model selection by the…

Diving into the planetary system of Proxima with NIRPS -- Breaking the metre per second barrier in the infrared
Alejandro Su\'arez Mascare\~no, \'Etienne Artigau, Lucile Mignon, Xavier Delfosse, Neil J. Cook, Fran\c{c}ois Bouchy, Ren\'e Doyon, Jonay I. Gonz\'alez Hern\'andez, Thomas Vandal, Izan de Castro Le\~ao, Atanas K. Stefanov, Jo\~ao Faria, Charles Cadieux, Pierrot Lamontagne, Fr\'ed\'erique Baron, Susana C. C. Barros, Bj\"orn Benneke, Xavier Bonf…

Diving into the planetary system of Proxima with NIRPS -- Breaking the metre per second barrier in the infrared
We obtained 420 high-resolution spectra of Proxima, over 159 nights, using the Near Infra Red Planet Searcher (NIRPS). We derived 149 nightly binned radial velocity measurements with a standard deviation of 1.69 m/s and a median uncertainty of 55 cm/s, and performed a joint analysis combining radial velocities, spectroscopic activity indicators, and ground-based photometry, to model the planetary and stellar signals present in the data, applying multi-dimensional Gaussian process regression to …

Fast and Efficient Implementation of the Maximum Likelihood Estimation for the Linear Regression with Gaussian Model Uncertainty
Ruohai Guo, Jiang Zhu, Xing Jiang, Fengzhong Qu
https://arxiv.org/abs/2507.11249

Fast and Efficient Implementation of the Maximum Likelihood Estimation for the Linear Regression with Gaussian Model Uncertainty
The linear regression model with a random variable (RV) measurement matrix, where the mean of the random measurement matrix has full column rank, has been extensively studied. In particular, the quasiconvexity of the maximum likelihood estimation (MLE) problem was established, and the corresponding Cramer-Rao bound (CRB) was derived, leading to the development of an efficient bisection-based algorithm known as RV-ML. In contrast, this work extends the analysis to both overdetermined and underde…

Test of partial effects for Frechet regression on Bures-Wasserstein manifolds
Haoshu Xu, Hongzhe Li
https://arxiv.org/abs/2506.23487 https://

Test of partial effects for Frechet regression on Bures-Wasserstein manifolds
We propose a novel test for assessing partial effects in Frechet regression on Bures Wasserstein manifolds. Our approach employs a sample splitting strategy: the first subsample is used to fit the Frechet regression model, yielding estimates of the covariance matrices and their associated optimal transport maps, while the second subsample is used to construct the test statistic. We prove that this statistic converges in distribution to a weighted mixture of chi squared components, where the wei…

Structural Extrapolation in Regression Discontinuity Designs with an Application to School Expenditure Referenda
Austin Feng, Francesco Ruggieri
https://arxiv.org/abs/2508.02658

Structural Extrapolation in Regression Discontinuity Designs with an Application to School Expenditure Referenda
We propose a structural approach to extrapolate average partial effects away from the cutoff in regression discontinuity designs (RDDs). Our focus is on applications that exploit closely contested school district referenda to estimate the effects of changes in education spending on local economic outcomes. We embed these outcomes in a spatial equilibrium model of local jurisdictions in which fiscal policy is determined by majority rule voting. This integration provides a microfoundation for the…

SABR-Informed Multitask Gaussian Process: A Synthetic-to-Real Framework for Implied Volatility Surface Construction
Jirong Zhuang, Xuan Wu
https://arxiv.org/abs/2506.22888

SABR-Informed Multitask Gaussian Process: A Synthetic-to-Real Framework for Implied Volatility Surface Construction
Constructing the Implied Volatility Surface (IVS) is a challenging task in quantitative finance due to the complexity of real markets and the sparsity of market data. Structural models like Stochastic Alpha Beta Rho (SABR) model offer interpretability and theoretical consistency but lack flexibility, while purely data-driven methods such as Gaussian Process regression can struggle with sparse data. We introduce SABR-Informed Multi-Task Gaussian Process (SABR-MTGP), treating IVS construction as …

Online design of experiments by active learning for nonlinear system identification
Kui Xie, Alberto Bemporad
https://arxiv.org/abs/2506.21754 https://

Online design of experiments by active learning for nonlinear system identification
We investigate the use of active-learning (AL) strategies to generate the input excitation signal at runtime for system identification of linear and nonlinear autoregressive and state-space models. We adapt various existing AL approaches for static model regression to the dynamic context, coupling them with a Kalman filter to update the model parameters recursively, and also cope with the presence of input and output constraints. We show the increased sample efficiency of the proposed approache…

Bayesian Regression Analysis with the Drift-Diffusion Model
Zekai Jin (Mental Health Data Science, New York State Psychiatric Institute, New York, USA), Yaakov Stern (Departments of Neurology, Columbia University Irving Medical Center, New York, USA), Seonjoo Lee (Mental Health Data Science, New York State Psychiatric Institute, New York, USA, Department of Biostatistics, Columbia University Irving Medical Center, New York, USA)

Bayesian Regression Analysis with the Drift-Diffusion Model
The Drift-Diffusion Model (DDM) is widely used in neuropsychological studies to understand the decision process by incorporating both reaction times and subjects' responses. Various models have been developed to estimate DDM parameters, with some employing Bayesian inference. However, when examining associations between phenotypes of interest and DDM parameters, most studies adopt a two-step approach: first estimating DDM parameters, then applying a separate statistical model to the estimated v…

Efficient Gibbs Sampling in Cox Regression Models Using Composite Partial Likelihood and P\'olya-Gamma Augmentation
Shu Tamano, Yui Tomo
https://arxiv.org/abs/2506.04675

Efficient Gibbs Sampling in Cox Regression Models Using Composite Partial Likelihood and Pólya-Gamma Augmentation
The Cox regression model and its Bayesian extensions are widely used in survival analysis. However, standard Bayesian approaches require modeling of the baseline hazard, and their full conditional distributions lack closed-form expressions. Therefore, the Metropolis-Hastings sampling algorithm is typically employed, whose efficiency is highly sensitive to the choice of proposal distribution. To address these issues, we propose the GS4Cox, an efficient Gibbs sampling algorithm for the Cox regres…

Predicting Stock Market Crash with Bayesian Generalised Pareto Regression
Sourish Das
https://arxiv.org/abs/2506.17549 https://arxiv.…

Predicting Stock Market Crash with Bayesian Generalised Pareto Regression
This paper develops a Bayesian Generalised Pareto Regression (GPR) model to forecast extreme losses in Indian equity markets, with a focus on the Nifty 50 index. Extreme negative returns, though rare, can cause significant financial disruption, and accurate modelling of such events is essential for effective risk management. Traditional Generalised Pareto Distribution (GPD) models often ignore market conditions; in contrast, our framework links the scale parameter to covariates using a log-line…

Latent Variable Autoregression with Exogenous Inputs
Daniil Bargman
https://arxiv.org/abs/2506.04488 https://arxiv.org/pdf/2506.04488…

Latent Variable Autoregression with Exogenous Inputs
This paper introduces a new least squares regression methodology called (C)LARX: a (constrained) latent variable autoregressive model with exogenous inputs. Two additional contributions are made as a side effect: First, a new matrix operator is introduced for matrices and vectors with blocks along one dimension; Second, a new latent variable regression (LVR) framework is proposed for economics and finance. The empirical section examines how well the stock market predicts real economic activity …

Machine-learning Growth at Risk
Tobias Adrian, Hongqi Chen, Max-Sebastian Dov\`i, Ji Hyung Lee
https://arxiv.org/abs/2506.00572 https://

Machine-learning Growth at Risk
We analyse growth vulnerabilities in the US using quantile partial correlation regression, a selection-based machine-learning method that achieves model selection consistency under time series. We find that downside risk is primarily driven by financial, labour-market, and housing variables, with their importance changing over time. Decomposing downside risk into its individual components, we construct sector-specific indices that predict it, while controlling for information from other sectors…

Stochastic Taylor expansion via Poisson point processes
Weichao Wu, Athanasios C. Micheas
https://arxiv.org/abs/2508.04703 https://arxiv.org/pdf/2508.04703…

Stochastic Taylor expansion via Poisson point processes
We generalize Taylor's theorem by introducing a stochastic formulation based on an underlying Poisson point process model. We utilize this approach to propose a novel non-linear regression framework and perform statistical inference of the model parameters. Theoretical properties of the proposed estimator are also proven, including its convergence, uniformly almost surely, to the true function. The theory is presented for the univariate and multivariate cases, and we exemplify the proposed meth…

AuralNet: Hierarchical Attention-based 3D Binaural Localization of Overlapping Speakers
Linya Fu, Yu Liu, Zhijie Liu, Zedong Yang, Zhong-Qiu Wang, Youfu Li, He Kong
https://arxiv.org/abs/2506.02773

AuralNet: Hierarchical Attention-based 3D Binaural Localization of Overlapping Speakers
We propose AuralNet, a novel 3D multi-source binaural sound source localization approach that localizes overlapping sources in both azimuth and elevation without prior knowledge of the number of sources. AuralNet employs a gated coarse-tofine architecture, combining a coarse classification stage with a fine-grained regression stage, allowing for flexible spatial resolution through sector partitioning. The model incorporates a multi-head self-attention mechanism to capture spatial cues in binaur…

Debiased Prediction Inference with Non-sparse Loadings in Misspecified High-dimensional Regression Models
Libin Liang, Zhiqiang Tan
https://arxiv.org/abs/2507.10944

Debiased Prediction Inference with Non-sparse Loadings in Misspecified High-dimensional Regression Models
High-dimensional regression models with regularized sparse estimation are widely applied. For statistical inferences, debiased methods are available about single coefficients or predictions with sparse new covariate vectors (also called loadings), in the presence of possible model misspecification. However, statistical inferences about predictions with non-sparse loadings are studied only under the assumption of correctly specified models. In this work, we develop debiased estimation and associ…

Testing Hypotheses of Covariate Effects on Topics of Discourse
Gabriel Phelan, David A. Campbell
https://arxiv.org/abs/2506.05570 https://

Testing Hypotheses of Covariate Effects on Topics of Discourse
We introduce an approach to topic modelling with document-level covariates that remains tractable in the face of large text corpora. This is achieved by de-emphasizing the role of parameter estimation in an underlying probabilistic model, assuming instead that the data come from a fixed but unknown distribution whose statistical functionals are of interest. We propose combining a convex formulation of non-negative matrix factorization with standard regression techniques as a fast-to-compute and…

Jet Reconstruction with Mamba Networks in Collider Events
Jinmian Li, Peng Li, Bingwei Long, Rao Zhang
https://arxiv.org/abs/2506.18336 https://

Jet Reconstruction with Mamba Networks in Collider Events
We introduce a novel end-to-end framework for jet reconstruction in high-energy collider events, leveraging the efficiency and long-range modeling capabilities of the Mamba architecture. Our model unifies instance segmentation, classification, and kinematic regression into a single multi-task learning system, enabling a sophisticated multi-level reconstruction that simultaneously identifies primary heavy jets ($t$, $H$, $W/Z$) and their constituent sub-jets. To facilitate supervised learning fo…

AICO: Feature Significance Tests for Supervised Learning
Kay Giesecke, Enguerrand Horel, Chartsiri Jirachotkulthorn
https://arxiv.org/abs/2506.23396 https:…

AICO: Feature Significance Tests for Supervised Learning
The opacity of many supervised learning algorithms remains a key challenge, hindering scientific discovery and limiting broader deployment -- particularly in high-stakes domains. This paper develops model- and distribution-agnostic significance tests to assess the influence of input features in any regression or classification algorithm. Our method evaluates a feature's incremental contribution to model performance by masking its values across samples. Under the null hypothesis, the distributio…

Symbolic Regression-Enhanced Dynamic Wake Meandering: Fast and Physically Consistent Wind-Turbine Wake Modeling
Ding Wang, Dachuan Feng, Kangcheng Zhou, Yuntian Chen, Shijun Liao, Shiyi Chen
https://arxiv.org/abs/2506.14403

Symbolic Regression-Enhanced Dynamic Wake Meandering: Fast and Physically Consistent Wind-Turbine Wake Modeling
Accurately modeling wind turbine wakes is essential for optimizing wind farm performance but remains a persistent challenge. While the dynamic wake meandering (DWM) model captures unsteady wake behavior, it suffers from near-wake inaccuracies due to empirical closures. We propose a Symbolic Regression-enhanced DWM (SRDWM) framework that achieves equation-level closure by embedding symbolic expressions for volumetric forcing and boundary terms explicitly into governing equations. These physicall…

This https://arxiv.org/abs/2107.04946 has been replaced.
link: https://scholar.google.com/scholar?q=a

Inference for the proportional odds cumulative logit model with monotonicity constraints for ordinal predictors and ordinal response
The proportional odds cumulative logit model (POCLM) is a standard regression model for an ordinal response. Ordinality of predictors can be incorporated by monotonicity constraints for the corresponding parameters. It is shown that estimators defined by optimization, such as maximum likelihood estimators, for an unconstrained model and for parameters in the interior set of the parameter space of a constrained model are asymptotically equivalent. This is used in order to derive asymptotic confi…

Strategies and statistical evaluation of Italy's regional model for COVID-19 restrictions
Giuseppe Drago, Giulia Marcon, Alberto Lombardo, Giuseppe Aiello
https://arxiv.org/abs/2507.02504

Strategies and statistical evaluation of Italy's regional model for COVID-19 restrictions
This study presents a comprehensive assessment of the Italian risk model used during the COVID-19 pandemic to guide regional mobility restrictions through a colour-coded classification system. The research focuses on evaluating the variables selected by the Italian Ministry of Health for this purpose and their effectiveness in supporting public health decision-making. The analysis adopts a statistical framework which combines data reduction and regression modelling techniques to enhance interpr…

Partially-shared Imaging Regression on Integrating Heterogeneous Brain-Cognition Associations across Alzheimer's Diagnoses
Yang Sui, Qi Xu, Ting Li, Yang Bai, Annie Qu
https://arxiv.org/abs/2505.24259

Partially-shared Imaging Regression on Integrating Heterogeneous Brain-Cognition Associations across Alzheimer's Diagnoses
This paper is motivated by the heterogeneous associations among demographic covariates, imaging data, and cognitive performances across different diagnostic groups within the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We propose a novel PArtially-shared Imaging Regression (PAIR) model with smooth spatial component integration to capture heterogeneous imaging coefficients across multiple data sources. The model assumes that each imaging coefficient can be represented as a weighted…

This https://arxiv.org/abs/2310.16207 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Propensity weighting plus adjustment in proportional hazards model is not doubly robust
Recently, it has become common for applied works to combine commonly used survival analysis modeling methods, such as the multivariable Cox model and propensity score weighting, with the intention of forming a doubly robust estimator of an exposure effect hazard ratio that is unbiased in large samples when either the Cox model or the propensity score model is correctly specified. This combination does not, in general, produce a doubly robust estimator, even after regression standardization, whe…

High-Dimensional Matrix-Variate Diffusion Index Models for Time Series Forecasting
Zhiren Ma, Qian Zhao, Riquan Zhang, Zhaoxing Gao
https://arxiv.org/abs/2508.04259 https://

High-Dimensional Matrix-Variate Diffusion Index Models for Time Series Forecasting
This paper proposes a novel diffusion-index model for forecasting when predictors are high-dimensional matrix-valued time series. We apply an $α$-PCA method to extract low-dimensional matrix factors and build a bilinear regression linking future outcomes to these factors, estimated via iterative least squares. To handle weak factor structures, we introduce a supervised screening step to select informative rows and columns. Theoretical properties, including consistency and asymptotic normality,…

Refining Tc Prediction in Hydrides via Symbolic-Regression-Enhanced Electron-Localization-Function-Based Descriptors
Francesco Belli, Sean Torres, Julia Contreras-Garc\`ia, Eva Zurek
https://arxiv.org/abs/2506.17456

Refining Tc Prediction in Hydrides via Symbolic-Regression-Enhanced Electron-Localization-Function-Based Descriptors
Hydrogen-based materials are able to possess extremely high superconducting critical temperatures, \tc s, due to hydrogen's low atomic mass and strong electron-phonon interaction. Recently, a descriptor based on the Electron Localization Function (ELF) has enabled the rapid estimation of the \tc\ of hydrogen-containing compounds from electronic networking properties, but its applicability has been limited by the small size and homogeneity of the training dataset used. Herein, the model is re-ex…

Oldies but Goldies: The Potential of Character N-grams for Romanian Texts
Dana Lupsa, Sanda-Maria Avram
https://arxiv.org/abs/2506.15650 https://

Oldies but Goldies: The Potential of Character N-grams for Romanian Texts
This study addresses the problem of authorship attribution for Romanian texts using the ROST corpus, a standard benchmark in the field. We systematically evaluate six machine learning techniques: Support Vector Machine (SVM), Logistic Regression (LR), k-Nearest Neighbors (k-NN), Decision Trees (DT), Random Forests (RF), and Artificial Neural Networks (ANN), employing character n-gram features for classification. Among these, the ANN model achieved the highest performance, including perfect clas…

This https://arxiv.org/abs/2504.14515 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A robust mixed-effects quantile regression model using generalized Laplace mixtures to handle outliers and skewness
Mixed-effects quantile regression models are widely used to capture heterogeneous responses in hierarchically structured data. The asymmetric Laplace (AL) distribution has traditionally served as the basis for quantile regression; however, its fixed skewness limits flexibility and renders it sensitive to outliers. In contrast, the generalized asymmetric Laplace (GAL) distribution enables more flexible modeling of skewness and heavy-tailed behavior, yet it remains vulnerable to extreme observati…

Probabilistic measures afford fair comparisons of AIWP and NWP model output
Tilmann Gneiting, Tobias Biegert, Kristof Kraus, Eva-Maria Walz, Alexander I. Jordan, Sebastian Lerch
https://arxiv.org/abs/2506.03744

Probabilistic measures afford fair comparisons of AIWP and NWP model output
We introduce a new measure for fair and meaningful comparisons of single-valued output from artificial intelligence based weather prediction (AIWP) and numerical weather prediction (NWP) models, called potential continuous ranked probability score (PC). In a nutshell, we subject the deterministic backbone of physics-based and data-driven models post hoc to the same statistical postprocessing technique, namely, isotonic distributional regression (IDR). Then we find PC as the mean continuous rank…

Orthogonality conditions for convex regression
Sheng Dai, Timo Kuosmanen, Xun Zhou
https://arxiv.org/abs/2506.21110 https://arxiv.org…

Orthogonality conditions for convex regression
Econometric identification generally relies on orthogonality conditions, which usually state that the random error term is uncorrelated with the explanatory variables. In convex regression, the orthogonality conditions for identification are unknown. Applying Lagrangian duality theory, we establish the sample orthogonality conditions for convex regression, including additive and multiplicative formulations of the regression model, with and without monotonicity and homogeneity constraints. We th…

A Framework for Nonstationary Gaussian Processes with Neural Network Parameters
Zachary James, Joseph Guinness
https://arxiv.org/abs/2507.12262 https://

A Framework for Nonstationary Gaussian Processes with Neural Network Parameters
Gaussian processes have become a popular tool for nonparametric regression because of their flexibility and uncertainty quantification. However, they often use stationary kernels, which limit the expressiveness of the model and may be unsuitable for many datasets. We propose a framework that uses nonstationary kernels whose parameters vary across the feature space, modeling these parameters as the output of a neural network that takes the features as input. The neural network and Gaussian proce…

Revisiting Randomization in Greedy Model Search
Xin Chen, Jason M. Klusowski, Yan Shuo Tan, Chang Yu
https://arxiv.org/abs/2506.15643 https://

Revisiting Randomization in Greedy Model Search
Combining randomized estimators in an ensemble, such as via random forests, has become a fundamental technique in modern data science, but can be computationally expensive. Furthermore, the mechanism by which this improves predictive performance is poorly understood. We address these issues in the context of sparse linear regression by proposing and analyzing an ensemble of greedy forward selection estimators that are randomized by feature subsampling -- at each iteration, the best feature is s…

Inference in a generalized Bradley-Terry model for paired comparisons with covariates and a growing number of subjects
Ting Yan
https://arxiv.org/abs/2507.22472 https://

Inference in a generalized Bradley-Terry model for paired comparisons with covariates and a growing number of subjects
Motivated by the home-field advantage in sports, we propose a generalized Bradley--Terry model that incorporates covariate information for paired comparisons. It has an $n$-dimensional merit parameter $\bsβ$ and a fixed-dimensional regression coefficient $\bsγ$ for covariates. When the number of subjects $n$ approaches infinity and the number of comparisons between any two subjects is fixed, we show the uniform consistency of the maximum likelihood estimator (MLE) $(\widehat{\bsβ}, \widehat{…

Bayesian Modeling of Long-Term Dynamics in Indian Temperature Extremes
Chitradipa Chakraborty
https://arxiv.org/abs/2507.01540 https://

Bayesian Modeling of Long-Term Dynamics in Indian Temperature Extremes
Annual maximum temperature data provides crucial insights into the impacts of climate change, especially for regions like India, where temperature variations have significant implications for agriculture, health, and infrastructure. In this study, we propose the Coupled Continuous Time Random Walk (CTRW) model to analyze annual maximum temperature data in India from 1901 to 2017 and compare its performance with the Bayesian Spectral Analysis Regression (BSAR) model. The CTRW model extends the s…

Regression Analysis of Reciprocity in Directed Networks
Rui Feng, Chenlei Leng
https://arxiv.org/abs/2507.21469 https://arxiv.org/pdf/2507.21469

Regression Analysis of Reciprocity in Directed Networks
Reciprocity--the tendency of individuals to form mutual ties--is a fundamental structural feature of many directed networks. Despite its ubiquity, reciprocity remains insufficiently integrated into statistical network models, particularly in relation to covariate information. In this paper, we introduce the $R^{2}$-Model, a novel and flexible framework that explicitly models reciprocity while incorporating covariate effects. Built upon a generalized $p_1$ model, our framework accommodates both …

Gradient Boosting for Spatial Regression Models with Autoregressive Disturbances
Michael Balzer
https://arxiv.org/abs/2506.13682 https://

Gradient Boosting for Spatial Regression Models with Autoregressive Disturbances
Researchers in urban and regional studies increasingly deal with spatial data that reflects geographic location and spatial relationships. As a framework for dealing with the unique nature of spatial data, various spatial regression models have been introduced. In this article, a novel model-based gradient boosting algorithm for spatial regression models with autoregressive disturbances is proposed. Due to the modular nature, the approach provides an alternative estimation procedure which is fe…

Clustering-based accelerometer measures to model relationships between physical activity and key outcomes
Hyatt Moore IV, Thomas N. Robinson, Alexandria Jensen, Fatma Gunturkun, K. Farish Haydel, Kristopher I Kapphahn, Manisha Desai
https://arxiv.org/abs/2507.00484

Clustering-based accelerometer measures to model relationships between physical activity and key outcomes
Accelerometers produce enormous amounts of data. Research that incorporates such data often involves a derived summary metric to describe physical activity. Traditional metrics have often ignored the temporal nature of the data. We build on previous work that applies unsupervised machine learning techniques to describe physical activity patterns over time. Specifically, we evaluate a summary measure of accelerometer data derived from unsupervised clustering in a regression framework through com…

Let the Tree Decide: FABART A Non-Parametric Factor Model
Sofia Velasco
https://arxiv.org/abs/2506.11551 https://arxiv.org/pdf/2506.1…

Let the Tree Decide: FABART A Non-Parametric Factor Model
This article proposes a novel framework that integrates Bayesian Additive Regression Trees (BART) into a Factor-Augmented Vector Autoregressive (FAVAR) model to forecast macro-financial variables and examine asymmetries in the transmission of oil price shocks. By employing nonparametric techniques for dimension reduction, the model captures complex, nonlinear relationships between observables and latent factors that are often missed by linear approaches. A simulation experiment comparing FABART…

High-dimensional regression with outcomes of mixed-type using the multivariate spike-and-slab LASSO
Soham Ghosh, Sameer K. Deshpande
https://arxiv.org/abs/2506.13007

High-dimensional regression with outcomes of mixed-type using the multivariate spike-and-slab LASSO
We consider a high-dimensional multi-outcome regression in which $q,$ possibly dependent, binary and continuous outcomes are regressed onto $p$ covariates. We model the observed outcome vector as a partially observed latent realization from a multivariate linear regression model. Our goal is to estimate simultaneously a sparse matrix ($B$) of latent regression coefficients (i.e., partial covariate effects) and a sparse latent residual precision matrix ($Ω$), which induces partial correlations …

Binary Response Forecasting under a Factor-Augmented Framework
Tingting Cheng, Jiachen Cong, Fei Liu, Xuanbin Yang
https://arxiv.org/abs/2507.16462 https:/…

Binary Response Forecasting under a Factor-Augmented Framework
In this paper, we propose a novel factor-augmented forecasting regression model with a binary response variable. We develop a maximum likelihood estimation method for the regression parameters and establish the asymptotic properties of the resulting estimators. Monte Carlo simulation results show that the proposed estimation method performs very well in finite samples. Finally, we demonstrate the usefulness of the proposed model through an application to U.S. recession forecasting. The proposed…

A Bayesian Additive Regression Trees Model for zero and one inflated data for Predicting Individual Treatment Effects in Alcohol Use Disorder Trials
Pamela Solano, M Lee Van Horn, Kyle Walters, Philipp Besendorfer, Alena Kuhlemeier, Manel Mart\'inez-Ram\'on, Thomas Jaki
https://arxiv.org/abs/2507.19848

A Bayesian Additive Regression Trees Model for zero and one inflated data for Predicting Individual Treatment Effects in Alcohol Use Disorder Trials
Alcohol Use Disorder (AUD) treatment presents high individual-level heterogeneity, with outcomes ranging from complete abstinence to persistent heavy drinking. This variability-driven by complex behavioral, social, and environmental factors-poses major challenges for treatment evaluation and individualized decision-making. In particular, accurately modeling bounded semicontinuous outcomes and estimating predictive individual treatment effects (PITEs) remains methodologically demanding. For th…

This https://arxiv.org/abs/2501.18221 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Network Weighted Functional Regression: a method for modeling dependencies between functional data in a network
This paper focuses on predicting continuous signals in a sensor lab network, particularly studying microclimate changes. We propose two novel concepts: Network Functional Data (NFD), which represents time series signals as functions on network nodes, and the Network Weighted Functional Regression (NWFR) model, which analyzes relationships between functional responses and predictors in a weighted network. Additionally, we introduce a functional conformal method to provide prediction bands with g…

Bayesian Variable Selection in Multivariate Regression Under Collinearity in the Design Matrix
Joyee Ghosh, Xun Li
https://arxiv.org/abs/2507.17975 https://

Bayesian Variable Selection in Multivariate Regression Under Collinearity in the Design Matrix
We consider the problem of variable selection in Bayesian multivariate linear regression models, involving multiple response and predictor variables, under multivariate normal errors. In the absence of a known covariance structure, specifying a model with a non-diagonal covariance matrix is appealing. Modeling dependency in the random errors through a non-diagonal covariance matrix is generally expected to lead to improved estimation of the regression coefficients. In this article, we highlight…

Shrinkage-Based Regressions with Many Related Treatments
Enes Dilber, Colin Gray
https://arxiv.org/abs/2507.01202 https://arxiv.org/p…

Shrinkage-Based Regressions with Many Related Treatments
When using observational causal models, practitioners often want to disentangle the effects of many related, partially-overlapping treatments. Examples include estimating treatment effects of different marketing touchpoints, ordering different types of products, or signing up for different services. Common approaches that estimate separate treatment coefficients are too noisy for practical decision-making. We propose a computationally light model that uses a customized ridge regression to move …

Mixtures of Neural Network Experts with Application to Phytoplankton Flow Cytometry Data
Ethan Pawl, Fran\c{c}ois Ribalet, Paul A. Parker, Sangwon Hyun
https://arxiv.org/abs/2507.01375

Mixtures of Neural Network Experts with Application to Phytoplankton Flow Cytometry Data
Flow cytometry is a valuable technique that measures the optical properties of particles at a single-cell resolution. When deployed in the ocean, flow cytometry allows oceanographers to study different types of photosynthetic microbes called phytoplankton. It is of great interest to study how phytoplankton properties change in response to environmental conditions. In our work, we develop a nonlinear mixture of experts model for simultaneous clustering and regression utilizing random-weight neur…

Causal Inference in Panel Data with a Continuous Treatment
Zhiguo Xiao, Peikai Wu
https://arxiv.org/abs/2506.23226 https://arxiv.org/…

Causal Inference in Panel Data with a Continuous Treatment
This paper proposes a framework that incorporates the two-way fixed effects model as a special case to conduct causal inference with a continuous treatment. Treatments are allowed to change over time and potential outcomes are dependent on historical treatments. Regression models on potential outcomes, along with the sequentially conditional independence assumptions (SCIAs) are introduced to identify the treatment effects, which are measured by aggre causal responses. Least squares and generali…

R2 priors for Grouped Variance Decomposition in High-dimensional Regression
Javier Enrique Aguilar, David Kohns, Aki Vehtari, Paul-Christian B\"urkner
https://arxiv.org/abs/2507.11833

R2 priors for Grouped Variance Decomposition in High-dimensional Regression
We introduce the Group-R2 decomposition prior, a hierarchical shrinkage prior that extends R2-based priors to structured regression settings with known groups of predictors. By decomposing the prior distribution of the coefficient of determination R2 in two stages, first across groups, then within groups, the prior enables interpretable control over model complexity and sparsity. We derive theoretical properties of the prior, including marginal distributions of coefficients, tail behavior, and …

TRUST: Transparent, Robust and Ultra-Sparse Trees
Albert Dorador
https://arxiv.org/abs/2506.15791 https://arxiv.org/pdf/2506.15791

TRUST: Transparent, Robust and Ultra-Sparse Trees
Piecewise-constant regression trees remain popular for their interpretability, yet often lag behind black-box models like Random Forest in predictive accuracy. In this work, we introduce TRUST (Transparent, Robust, and Ultra-Sparse Trees), a novel regression tree model that combines the accuracy of Random Forests with the interpretability of shallow decision trees and sparse linear models. TRUST further enhances transparency by leveraging Large Language Models to generate tailored, user-friendl…

Bayesian wavelet shrinkage for low SNR data based on the Epanechnikov kernel
Fidel Aniano Causil Barrios, Alex Rodrigo dos Santos Sousa
https://arxiv.org/abs/2507.11718

Bayesian wavelet shrinkage for low SNR data based on the Epanechnikov kernel
Consider the univariate nonparametric regression model with additive Gaussian noise and the representation of the unknown regression function in terms of a wavelet basis. We propose a shrinkage rule to estimate the wavelet coefficients obtained by mixing a point mass function at zero with the Epanechnikov distribution as a prior for the coefficients. The proposed rule proved to be suitable for application in scenarios with low signal-to-noise ratio datasets and outperformed standard and Bayesia…

Predictive Causal Inference via Spatio-Temporal Modeling and Penalized Empirical Likelihood
Byunghee Lee, Hye Yeon Sin, Joonsung Kang
https://arxiv.org/abs/2507.08896

Predictive Causal Inference via Spatio-Temporal Modeling and Penalized Empirical Likelihood
This study introduces an integrated framework for predictive causal inference designed to overcome limitations inherent in conventional single model approaches. Specifically, we combine a Hidden Markov Model (HMM) for spatial health state estimation with a Multi Task and Multi Graph Convolutional Network (MTGCN) for capturing temporal outcome trajectories. The framework asymmetrically treats temporal and spatial information regarding them as endogenous variables in the outcome regression, and e…