Tootfinder

An Easily Tunable Approach to Robust and Sparse High-Dimensional Linear Regression
Takeyuki Sasai, Hironori Fujisawa
https://arxiv.org/abs/2506.12591 https…

An Easily Tunable Approach to Robust and Sparse High-Dimensional Linear Regression
Sparse linear regression methods such as Lasso require a tuning parameter that depends on the noise variance, which is typically unknown and difficult to estimate in practice. In the presence of heavy-tailed noise or adversarial outliers, this problem becomes more challenging. In this paper, we propose an estimator for robust and sparse linear regression that eliminates the need for explicit prior knowledge of the noise scale. Our method builds on the Huber loss and incorporates an iterative sc…

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 …

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure
Micha{\l} Derezi\'nski, Aaron Sidford
https://arxiv.org/abs/2507.11724 …

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure
We provide new high-accuracy randomized algorithms for solving linear systems and regression problems that are well-conditioned except for $k$ large singular values. For solving such $d \times d$ positive definite system our algorithms succeed whp. and run in time $\tilde O(d^2 + k^ω)$. For solving such regression problems in a matrix $\mathbf{A} \in \mathbb{R}^{n \times d}$ our methods succeed whp. and run in time $\tilde O(\mathrm{nnz}(\mathbf{A}) + d^2 + k^ω)$ where $ω$ is the matrix mult…

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…

Algorithm Design and Comparative Test of Natural Gradient Gaussian Approximation Filter
Wenhan Cao, Tianyi Zhang, Shengbo Eben Li
https://arxiv.org/abs/2507.11872

Algorithm Design and Comparative Test of Natural Gradient Gaussian Approximation Filter
Popular Bayes filters typically rely on linearization techniques such as Taylor series expansion and stochastic linear regression to use the structure of standard Kalman filter. These techniques may introduce large estimation errors in nonlinear and non-Gaussian systems. This paper overviews a recent breakthrough in filtering algorithm design called \textit{N}atural Gr\textit{a}dient Gaussia\textit{n} Appr\textit{o}ximation (NANO) filter and compare its performance over a large class of nonline…

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…

To What Extent Can Public Equity Indices Statistically Hedge Real Purchasing Power Loss in Compounded Structural Emerging-Market Crises? An Explainable ML-Based Assessment
Artem Alkhamov, Boris Kriuk
https://arxiv.org/abs/2507.13055

To What Extent Can Public Equity Indices Statistically Hedge Real Purchasing Power Loss in Compounded Structural Emerging-Market Crises? An Explainable ML-Based Assessment
This study investigates the extent to which local public equity indices can statistically hedge real purchasing power loss during compounded structural macro-financial collapses in emerging markets. We employ a non-linear multiplicative real return calculations consistent with Fisher-parity logics for both domestic and foreign investors with a principled quantile regression, tail dependence copula analysis, and Shapley Additive Explanations (SHAP) to assess the explanatory power of macro variab…

Approximating Euler Totient Function using Linear Regression on RSA moduli
Gilda Rech Bansimba, Regis F. Babindamana, Beni Blaug N. Ibara
https://arxiv.org/abs/2507.06706

Approximating Euler Totient Function using Linear Regression on RSA moduli
The security of the RSA cryptosystem is based on the intractability of computing Euler's totient function phi(n) for large integers n. Although deriving phi(n) deterministically remains computationally infeasible for cryptographically relevant bit lengths, and machine learning presents a promising alternative for constructing efficient approximations. In this work, we explore a machine learning approach to approximate Euler's totient function phi using linear regression models. We consider a da…

Let's play POLO: Integrating the probability of lesion origin into proton treatment plan optimization for low-grade glioma patients
Tim Ortkamp, Habiba Sallem, Semi Harrabi, Martin Frank, Oliver J\"akel, Julia Bauer, Niklas Wahl
https://arxiv.org/abs/2506.13539

Let's play POLO: Integrating the probability of lesion origin into proton treatment plan optimization for low-grade glioma patients
In proton therapy of low-grade glioma (LGG) patients, contrast-enhancing brain lesions (CEBLs) on magnetic resonance imaging are considered predictive of late radiation-induced lesions. From the observation that CEBLs tend to concentrate in regions of increased dose-averaged linear energy transfer (LET) and proximal to the ventricular system, the probability of lesion origin (POLO) model has been established as a multivariate logistic regression model for the voxel-wise probability prediction o…

Inductance Estimation for High-Power Multilayer Rectangle Planar Windings
Theofilos Papadopoulos, Antonios Antonopoulos
https://arxiv.org/abs/2507.12082 ht…

Inductance Estimation for High-Power Multilayer Rectangle Planar Windings
This paper proposes a simple and accurate monomial-like equation for estimating the inductance of Multilayer Rectangle-shaped Planar Windings (MLRPWs) for high-frequency, high-power applications. The equation consists of the power product of the geometrical dimensions, raised at individual power coefficients. The coefficients are generated via Multiple Linear Regression (MLR), based on a large set of approximately 6,000 simulated windings, with an 80/20 training/evaluation sample ratio. The res…

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…

Black hole/quantum machine learning correspondence
Jae-Weon Lee, Zae Young Kim
https://arxiv.org/abs/2506.09678 https://arxiv.org/pdf…

Black hole/quantum machine learning correspondence
We explore a potential connection between the black hole information paradox and the double descent phenomenon in quantum machine learning. Information retrieval from Hawking radiation can be viewed through the lens of quantum linear regression over black hole microstates, with the Page time corresponding to the interpolation threshold, beyond which test error decreases despite overparameterization. Using the Marchenko-Pastur law, we derive the variance in test error for the quantum linear regr…

Correction of estimator bias in linear regression with categorical covariates with classification error
Alexandre Garcia Dias, Mariana Rodrigues Motta, Alexandre Hild Aono
https://arxiv.org/abs/2507.07245

Correction of estimator bias in linear regression with categorical covariates with classification error
The objective of this work is to propose an asymptotic correction method for the estimators of parameters from regression models with covariates subject to classification errors. A correction was developed based on the least squares estimators from regression with erroneous covariates, the marginal probability of the true covariates, and the conditional probability of the erroneous covariates given the true covariates. In this way, we can correct these estimators without the need to correct the…

Semi-parametric Functional Classification via Path Signatures Logistic Regression
Pengcheng Zeng, Siyuan Jiang
https://arxiv.org/abs/2507.06637 https://

Semi-parametric Functional Classification via Path Signatures Logistic Regression
We propose Path Signatures Logistic Regression (PSLR), a semi-parametric framework for classifying vector-valued functional data with scalar covariates. Classical functional logistic regression models rely on linear assumptions and fixed basis expansions, which limit flexibility and degrade performance under irregular sampling. PSLR overcomes these issues by leveraging truncated path signatures to construct a finite-dimensional, basis-free representation that captures nonlinear and cross-channe…

This https://arxiv.org/abs/2506.03100 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds
Retrieval-augmented generation (RAG) has seen many empirical successes in recent years by aiding the LLM with external knowledge. However, its theoretical aspect has remained mostly unexplored. In this paper, we propose the first finite-sample generalization bound for RAG in in-context linear regression and derive an exact bias-variance tradeoff. Our framework views the retrieved texts as query-dependent noisy in-context examples and recovers the classical in-context learning (ICL) and standard…

Fine-Grained control over Music Generation with Activation Steering
Dipanshu Panda, Jayden Koshy Joe, Harshith M R, Swathi Narashiman, Pranay Mathur, Anish Veerakumar, Aniruddh Krishna, Keerthiharan A
https://arxiv.org/abs/2506.10225

Fine-Grained control over Music Generation with Activation Steering
We present a method for fine-grained control over music generation through inference-time interventions on an autoregressive generative music transformer called MusicGen. Our approach enables timbre transfer, style transfer, and genre fusion by steering the residual stream using weights of linear probes trained on it, or by steering the attention layer activations in a similar manner. We observe that modelling this as a regression task provides improved performance, hypothesizing that the mean-…

GP-Recipe: Gaussian Process approximation to linear operations in numerical methods
Christopher DeGrendele, Dongwook Lee
https://arxiv.org/abs/2506.03471 h…

GP-Recipe: Gaussian Process approximation to linear operations in numerical methods
We introduce new Gaussian Process (GP) high-order approximations to linear operations that are frequently used in various numerical methods. Our method employs the kernel-based GP regression modeling, a non-parametric Bayesian approach to regression that operates on the probability distribution over all admissible functions that fit observed data. We begin in the first part with discrete data approximations to various linear operators applied to smooth data using the most popular squared expone…

Forward Variable Selection in Ultra-High Dimensional Linear Regression Using Gram-Schmidt Orthogonalization
Jialuo Chen, Zhaoxing Gao, Ruey S. Tsay
https://arxiv.org/abs/2507.04668

Forward Variable Selection in Ultra-High Dimensional Linear Regression Using Gram-Schmidt Orthogonalization
We investigate forward variable selection for ultra-high dimensional linear regression using a Gram-Schmidt orthogonalization procedure. Unlike the commonly used Forward Regression (FR) method, which computes regression residuals using an increasing number of selected features, or the Orthogonal Greedy Algorithm (OGA), which selects variables based on their marginal correlations with the residuals, our proposed Gram-Schmidt Forward Regression (GSFR) simplifies the selection process by evaluatin…

Evidence for an intrinsic luminosity-decay correlation in GRB radio afterglows
S. P. R. Shilling, S. R. Oates, D. A. Kann, J. Patel, J. L. Racusin, B. Cenko, R. Gupta, M. Smith, L. Rhodes, K. R. Hinds, M. Nicholl, A. Breeveld, M. Page, M. De Pasquale, B. Gompertz
https://arxiv.org/abs/2508.07276

Evidence for an intrinsic luminosity-decay correlation in GRB radio afterglows
We present the discovery of a correlation, in a sample of 16 gamma-ray burst 8.5 GHz radio afterglows, between the intrinsic luminosity measured at 10 days in the rest frame, $L_{\mathrm{Radio,10d}}$, and the average rate of decay past this time, $α_{>10d}$. The correlation has a Spearman's rank coefficient of $-0.70 \pm 0.13$ at a significance of $>3σ$ and a linear regression fit of $α_{>10d} = -0.29^{+0.19}_{-0.28} \log \left(L_{\mathrm{Radio,10d}} \right) + 8.12^{+8.86}_{-5.88}$. This fin…

Standard LSParameter Estimators Ensure Finite Convergence Time for Linear Regression Equations Under an Interval Excitation Assumption
Romeo Ortega, Jose Guadalupe Romero, Stanislav Aranovskiy, Gang Tao
https://arxiv.org/abs/2506.08211

Standard LSParameter Estimators Ensure Finite Convergence Time for Linear Regression Equations Under an Interval Excitation Assumption
In this brief note we recall the little-known fact that, for linear regression equations (LRE) with intervally excited (IE) regressors, standard Least Square (LS) parameter estimators ensure finite convergence time (FCT) of the estimated parameters. The convergence time being equal to the time length needed to comply with the IE assumption. As is well-known, IE is necessary and sufficient for the identifiability of the LRE-hence, it is the weakest assumption for the on-or off-line solution of t…

Replaced article(s) found for econ.EM. https://arxiv.org/list/econ.EM/new
[1/1]:
- Nonparametric Treatment Effect Identification in School Choice
Jiafeng Chen
https://arxiv.org/abs/2112.03872
- Potential weights and implicit causal designs in linear regression
Jiafeng Chen
https://arxiv.org/abs/2407.21119 https://mastoxiv.page/@arXiv_econEM_bot/112885535340833628
- Reinterpreting demand estimation
Jiafeng Chen
https://arxiv.org/abs/2503.23524 https://mastoxiv.page/@arXiv_econEM_bot/114262944644769755
- A step towards the integration of machine learning and classic model-based survey methods
Tomasz \.Z\k{a}d{\l}o, Adam Chwila
https://arxiv.org/abs/2402.07521 https://mastoxiv.page/@arXiv_statME_bot/111924442633277025
- Galerkin-ARIMA: A Two-Stage Polynomial Regression Framework for Fast Rolling One-Step-Ahead Forec...
Haojie Liu, Zihan Lin
https://arxiv.org/abs/2507.07469 https://mastoxiv.page/@arXiv_statML_bot/114833708254543213
toXiv_bot_toot

Machine learning-based correlation analysis of decadal cyclone intensity with sea surface temperature: data and tutorial
Jingyang Wu, Rohitash Chandra
https://arxiv.org/abs/2506.09254

Machine learning-based correlation analysis of decadal cyclone intensity with sea surface temperature: data and tutorial
The rising number of extreme climate events in the past decades has motivated the need for a thorough consideration of tropical cyclone genesis and intensity, given the sea-surface temperature (SST). In this paper, we present an analysis of the relationship between the increasing global SST with cyclone genesis using linear regression machine learning models. We extract and curate a dataset of tropical cyclones across selected ocean basins with their associated SST over the past 40 years. We pr…

A new sparsity promoting residual transform operator for Lasso regression
Yao Xiao, Anne Gelb, Aditya Viswanathan
https://arxiv.org/abs/2506.22689 https://…

A new sparsity promoting residual transform operator for Lasso regression
Lasso regression is a widely employed approach within the $\ell_1$ regularization framework used to promote sparsity and recover piecewise smooth signals $f:[a,b) \rightarrow \mathbb{R}$ when the given observations are obtained from noisy, blurred, and/or incomplete data environments. In choosing the regularizing sparsity-promoting operator, it is assumed that the particular type of variability of the underlying signal, for example, piecewise constant or piecewise linear behavior across the ent…

GPU-Parallelizable Randomized Sketch-and-Precondition for Linear Regression using Sparse Sign Sketches
Tyler Chen, Pradeep Niroula, Archan Ray, Pragna Subrahmanya, Marco Pistoia, Niraj Kumar
https://arxiv.org/abs/2506.03070

GPU-Parallelizable Randomized Sketch-and-Precondition for Linear Regression using Sparse Sign Sketches
A litany of theoretical and numerical results have established the sketch-and-precondition paradigm as a powerful approach to solving large linear regression problems in standard computing environments. Perhaps surprisingly, much less work has been done on understanding how sketch-and-precondition performs on graphics processing unit (GPU) systems. We address this gap by benchmarking an implementation of sketch-and-precondition based on sparse sign-sketches on single and multi-GPU systems. In d…

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

Serum 25-hydroxyvitamin D concentration is not associated with mental health among Aboriginal and Torres Strait Islander Peoples in Australia: a cross-sectional exploratory study
Belinda Neo (Curtin Medical School, Curtin University, Bentley, Western Australia, Australia), Noel Nannup (The Kids Research Institute Australia, Nedlands, Western Australia, Australia), Dale Tilbrook (Maalinup Aboriginal Gallery, Caversham, Western Australia, Australia), Carol Michie (The Kids Research Insti…

Serum 25-hydroxyvitamin D concentration is not associated with mental health among Aboriginal and Torres Strait Islander Peoples in Australia: a cross-sectional exploratory study
Objective: To investigate the association between serum 25-hydroxyvitamin D [25(OH)D] concentration and mental health, measured using the Kessler Psychological Distress Scale 5 (K5), among Aboriginal and Torres Strait Islander Peoples. Methods: We used cross-sectional data from the 2012-2013 Australian Aboriginal and Torres Strait Islander Health Survey. Multiple linear regression was used to test the association between serum 25(OH)D concentration and K5, adjusting for age, sex, education, rem…

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…

Incremental Seeded EM Algorithm for Clusterwise Linear Regression
Ye Chow Kuang, Melanie Ooi
https://arxiv.org/abs/2507.04629 https://

Incremental Seeded EM Algorithm for Clusterwise Linear Regression
This paper proposes Incremental Seeded Expectation Maximization, an algorithm that improves upon the traditional Expectation Maximization computational flow for clusterwise or finite mixture linear regression tasks. The proposed method shows significantly better performance, particularly in scenarios involving high-dimensional input, noisy data, or a large number of clusters. Alongside the new algorithm, this paper introduces the concepts of $\textit{Resolvability}$ and $\textit{X-predictabilit…

A Regression-Based Share Market Prediction Model for Bangladesh
Syeda Tasnim Fabiha, Rubaiyat Jahan Mumu, Farzana Aktar, B M Mainul Hossain
https://arxiv.org/abs/2507.18643 http…

A Regression-Based Share Market Prediction Model for Bangladesh
Share market is one of the most important sectors of economic development of a country. Everyday almost all companies issue their shares and investors buy and sell shares of these companies. Generally investors want to buy shares of the companies whose market liquidity is comparatively greater. Market liquidity depends on the average price of a share. In this paper, a thorough linear regression analysis has been performed on the stock market data of Dhaka Stock Exchange. Later, the linear model…

CLT in high-dimensional Bayesian linear regression with low SNR
Seunghyun Lee, Nabarun Deb, Sumit Mukherjee
https://arxiv.org/abs/2507.23285 https://arxiv.…

CLT in high-dimensional Bayesian linear regression with low SNR
We study central limit theorems for linear statistics in high-dimensional Bayesian linear regression with product priors. Unlike the existing literature where the focus is on posterior contraction, we work under a non-contracting regime where neither the likelihood nor the prior dominates the other. This is motivated by modern high-dimensional datasets characterized by a bounded signal-to-noise ratio. This work takes a first step towards understanding limit distributions for one-dimensional pro…

This https://arxiv.org/abs/2303.11721 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Using Forests in Multivariate Regression Discontinuity Designs
We discuss estimation and inference of conditional treatment effects in regression discontinuity (RD) designs with multiple scores. In addition to local linear regressions and the minimax-optimal estimator more recently proposed by Imbens and Wager (2019), we argue that two variants of random forests, honest regression forests and local linear forests, should be added to the toolkit of applied researchers working with multivariate RD designs; their validity follows from results in Wager and Ath…

Bayesian Models for Joint Selection of Features and Auto-Regressive Lags: Theory and Applications in Environmental and Financial Forecasting
Alokesh Manna, Sujit K. Ghosh
https://arxiv.org/abs/2508.10055

Bayesian Models for Joint Selection of Features and Auto-Regressive Lags: Theory and Applications in Environmental and Financial Forecasting
We develop a Bayesian framework for variable selection in linear regression with autocorrelated errors, accommodating lagged covariates and autoregressive structures. This setting occurs in time series applications where responses depend on contemporaneous or past explanatory variables and persistent stochastic shocks, including financial modeling, hydrological forecasting, and meteorological applications requiring temporal dependency capture. Our methodology uses hierarchical Bayesian models w…

Training-Free Voice Conversion with Factorized Optimal Transport
Alexander Lobashev, Assel Yermekova, Maria Larchenko
https://arxiv.org/abs/2506.09709 http…

Training-Free Voice Conversion with Factorized Optimal Transport
This paper introduces Factorized MKL-VC, a training-free modification for kNN-VC pipeline. In contrast with original pipeline, our algorithm performs high quality any-to-any cross-lingual voice conversion with only 5 second of reference audio. MKL-VC replaces kNN regression with a factorized optimal transport map in WavLM embedding subspaces, derived from Monge-Kantorovich Linear solution. Factorization addresses non-uniform variance across dimensions, ensuring effective feature transformation.…

High-dimensional Longitudinal Inference via a De-sparsified Dantzig-Selector
Nathan Huey, Rajarshi Mukherjee
https://arxiv.org/abs/2508.07498 https://arxiv…

High-dimensional Longitudinal Inference via a De-sparsified Dantzig-Selector
In this paper, we consider statistical inference with generalized linear models in high dimensions under a longitudinal clustered data framework. Specifically, we propose a de-sparsified version of an initial Dantzig-type regularized estimator in regression settings and provide theoretical justification for both linear and generalized linear models. We present extensive numerical simulations demonstrating the effectiveness of our method for continuous and binary data. For continuous outcomes un…

Explainability-Driven Feature Engineering for Mid-Term Electricity Load Forecasting in ERCOT's SCENT Region
Abhiram Bhupatiraju, Sung Bum Ahn
https://arxiv.org/abs/2507.22220

Explainability-Driven Feature Engineering for Mid-Term Electricity Load Forecasting in ERCOT's SCENT Region
Accurate load forecasting is essential to the operation of modern electric power systems. Given the sensitivity of electricity demand to weather variability and temporal dynamics, capturing non-linear patterns is essential for long-term planning. This paper presents a comparative analysis of machine learning models, Linear Regression, XGBoost, LightGBM, and Long Short-Term Memory (LSTM), for forecasting system-wide electricity load up to one year in advance. Midterm forecasting has shown to be …

Heavy Lasso: sparse penalized regression under heavy-tailed noise via data-augmented soft-thresholding
The Tien Mai
https://arxiv.org/abs/2506.07790 https:…

Heavy Lasso: sparse penalized regression under heavy-tailed noise via data-augmented soft-thresholding
High-dimensional linear regression is a fundamental tool in modern statistics, particularly when the number of predictors exceeds the sample size. The classical Lasso, which relies on the squared loss, performs well under Gaussian noise assumptions but often deteriorates in the presence of heavy-tailed errors or outliers commonly encountered in real data applications such as genomics, finance, and signal processing. To address these challenges, we propose a novel robust regression method, terme…

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…

Replaced article(s) found for q-fin.MF. https://arxiv.org/list/q-fin.MF/new
[1/1]:
- An analysis of linear regression and neural networks approximation for the pricing of swing options
Christian Yeo

Identifiability in Unlinked Linear Regression: Some Results and Open Problems
Fadoua Balabdaoui, Martin Slawski, Jonathan Steffani
https://arxiv.org/abs/2507.14986

Identifiability in Unlinked Linear Regression: Some Results and Open Problems
A tacit assumption in classical linear regression problems is the full knowledge of the existing link between the covariates and responses. In Unlinked Linear Regression (ULR) this link is either partially or completely missing. While the reasons causing such missingness can be different, a common challenge in statistical inference is the potential non-identifiability of the regression parameter. In this note, we review the existing literature on identifiability when the $d \ge 2$ components of…

Comparing Building Thermal Dynamics Models and Estimation Methods for Grid-Edge Applications
Ninad Gaikwad, Kunal Shankar, Anamika Dubey, Alan Love, Olvar Bergland
https://arxiv.org/abs/2508.09118

Comparing Building Thermal Dynamics Models and Estimation Methods for Grid-Edge Applications
We need computationally efficient and accurate building thermal dynamics models for use in grid-edge applications. This work evaluates two grey-box approaches for modeling building thermal dynamics: RC-network models and structured regression models. For RC-network models, we compare parameter estimation methods including Nonlinear Least Squares, Batch Estimation, and Maximum Likelihood Estimation. We use the Almon Lag Structure with Linear Least Squares for estimating the structured regression…

Semi-supervised learning for linear extremile regression
Rong Jiang, Keming Yu, Jiangfeng Wang
https://arxiv.org/abs/2507.01314 https://

Semi-supervised learning for linear extremile regression
Extremile regression, as a least squares analog of quantile regression, is potentially useful tool for modeling and understanding the extreme tails of a distribution. However, existing extremile regression methods, as nonparametric approaches, may face challenges in high-dimensional settings due to data sparsity, computational inefficiency, and the risk of overfitting. While linear regression serves as the foundation for many other statistical and machine learning models due to its simplicity, …

Does Trump's Tariff Make America Great Again? An Empirical Analysis of US-China Trade War Impact on American Business Formation (2018-2025)
Ruiming Min
https://arxiv.org/abs/2506.00999

Does Trump's Tariff Make America Great Again? An Empirical Analysis of US-China Trade War Impact on American Business Formation (2018-2025)
This study examines whether Donald Trump's tariff policies, particularly the US-China trade war initiated in 2018, delivered on promises to revitalize American manufacturing and create jobs. Using county-level business application data from 2018-2025, we analyze the relationship between tariff implementation and new business formation through linear regression analysis. Our findings reveal a statistically significant positive association between US tariffs on China and American business applica…

QUTCC: Quantile Uncertainty Training and Conformal Calibration for Imaging Inverse Problems
Cassandra Tong Ye, Shamus Li, Tyler King, Kristina Monakhova
https://arxiv.org/abs/2507.14760

QUTCC: Quantile Uncertainty Training and Conformal Calibration for Imaging Inverse Problems
Deep learning models often hallucinate, producing realistic artifacts that are not truly present in the sample. This can have dire consequences for scientific and medical inverse problems, such as MRI and microscopy denoising, where accuracy is more important than perceptual quality. Uncertainty quantification techniques, such as conformal prediction, can pinpoint outliers and provide guarantees for image regression tasks, improving reliability. However, existing methods utilize a linear consta…

Maximum-likelihood reprojections for reliable Koopman-based predictions and bifurcation analysis of parametric dynamical systems
Pieter van Goor, Robert Mahony, Manuel Schaller, Karl Worthmann
https://arxiv.org/abs/2506.17817

Maximum-likelihood reprojections for reliable Koopman-based predictions and bifurcation analysis of parametric dynamical systems
Koopman-based methods leverage a nonlinear lifting to enable linear regression techniques. Consequently, data generation, learning and prediction is performed through the lens of this lifting, giving rise to a nonlinear manifold that is invariant under the Koopman operator. In data-driven approximation such as Extended Dynamic Mode Decomposition, this invariance is typically lost due to the presence of (finite-data) approximation errors. In this work, we show that reprojections are crucial for …

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling
Saurabh Rane
https://arxiv.org/abs/2508.00200 https://

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling
Formula 1 performance is a combination of the car's ability and the driver's ability. While a given race or season can tell you how well a car and driver performed jointly, isolating the individual impact of the driver and constructor remains challenging. This paper extends a Regularized Adjusted Plus Minus (RAPM) methodology (Sill 2010), commonly used in basketball and hockey, to parse out individual driver and constructor impact. It employs a time-decayed ridge regression with LOESS (Jacoby 2…

Optimal Exact Designs of Multiresponse Experiments under Linear and Sparsity Constraints
Lenka Filov\'a, P\'al Somogyi, Radoslav Harman
https://arxiv.org/abs/2507.04713

Optimal Exact Designs of Multiresponse Experiments under Linear and Sparsity Constraints
We propose a computational approach to constructing exact designs on finite design spaces that are optimal for multiresponse regression experiments under a combination of the standard linear and specific 'sparsity' constraints. The linear constraints address, for example, limits on multiple resource consumption and the problem of optimal design augmentation, while the sparsity constraints control the set of distinct trial conditions utilized by the design. The key idea is to construct an artifi…

Analysis of Photonic Circuit Losses with Machine Learning Techniques
Adrian Nugraha Utama, Simon Chun Kiat Goh, Li Hongyu, Wang Xiangyu, Zhou Yanyan, Victor Leong, Manas Mukherjee
https://arxiv.org/abs/2506.17999

Analysis of Photonic Circuit Losses with Machine Learning Techniques
Low-loss waveguides enable efficient light delivery in photonic circuits, which are essential for high-speed optical communications and scalable implementations of photonic quantum technologies. We study the effects of several fabrication and experimental parameters on the waveguide losses of a silicon nitride integrated photonics platform using various machine learning techniques. Compared to more complex machine learning algorithms, our results show that a simple linear regression model with …

Adaptive Market Intelligence: A Mixture of Experts Framework for Volatility-Sensitive Stock Forecasting
Diego Vallarino
https://arxiv.org/abs/2508.02686 https://

Adaptive Market Intelligence: A Mixture of Experts Framework for Volatility-Sensitive Stock Forecasting
This study develops and empirically validates a Mixture of Experts (MoE) framework for stock price prediction across heterogeneous volatility regimes using real market data. The proposed model combines a Recurrent Neural Network (RNN) optimized for high-volatility stocks with a linear regression model tailored to stable equities. A volatility-aware gating mechanism dynamically weights the contributions of each expert based on asset classification. Using a dataset of 30 publicly traded U.S. stoc…

Covariance scanning for adaptively optimal change point detection in high-dimensional linear models
Haeran Cho, Housen Li
https://arxiv.org/abs/2507.02552 …

Covariance scanning for adaptively optimal change point detection in high-dimensional linear models
This paper investigates the detection and estimation of a single change in high-dimensional linear models. We derive minimax lower bounds for the detection boundary and the estimation rate, which uncover a phase transition governed the sparsity of the covariance-weighted differential parameter. This form of "inherent sparsity" captures a delicate interplay between the covariance structure of the regressors and the change in regression coefficients on the detectability of a change point. Complem…

Empirical Models of the Time Evolution of SPX Option Prices
Alessio Brini, David A. Hsieh, Patrick Kuiper, Sean Moushegian, David Ye
https://arxiv.org/abs/2506.17511

Empirical Models of the Time Evolution of SPX Option Prices
The key objective of this paper is to develop an empirical model for pricing SPX options that can be simulated over future paths of the SPX. To accomplish this, we formulate and rigorously evaluate several statistical models, including neural network, random forest, and linear regression. These models use the observed characteristics of the options as inputs -- their price, moneyness and time-to-maturity, as well as a small set of external inputs, such as the SPX and its past history, dividend …

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…

Wild Bootstrap Inference for Linear Regressions with Many Covariates
Wenze Li
https://arxiv.org/abs/2506.20972 https://arxiv.org/pdf/…

Wild Bootstrap Inference for Linear Regressions with Many Covariates
We propose a simple modification to the wild bootstrap procedure and establish its asymptotic validity for linear regression models with many covariates and heteroskedastic errors. Monte Carlo simulations show that the modified wild bootstrap has excellent finite sample performance compared with alternative methods that are based on standard normal critical values, especially when the sample size is small and/or the number of controls is of the same order of magnitude as the sample size.

Proof of The TAP Free Energy for High-Dimensional Linear Regression with Spherical Priors at All Temperatures
Zhiyuan Yu, Jingbo Liu
https://arxiv.org/abs/2506.20768

Proof of The TAP Free Energy for High-Dimensional Linear Regression with Spherical Priors at All Temperatures
Variational inference (VI) has emerged as an efficient framework for approximating intractable posteriors. Traditional mean-field VI methods fail to capture the complex dependencies and can be inconsistent in high-dimensional settings. In contrast, it has been conjectured, based on statistical physics heuristics and conditions for the stationary points, that the more refined Thouless-Anderson-Palmer (TAP) approximation is consistent in the high-dimensional regimes. Existing results, however, ha…

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…

A powerful transformation of quantitative responses for biobank-scale association studies
Yaowu Liu, Tianying Wang
https://arxiv.org/abs/2507.06496 https:/…

A powerful transformation of quantitative responses for biobank-scale association studies
In linear regression models with non-Gaussian errors, transformations of the response variable are widely used in a broad range of applications. Motivated by various genetic association studies, transformation methods for hypothesis testing have received substantial interest. In recent years, the rise of biobank-scale genetic studies, which feature a vast number of participants that could be around half a million, spurred the need for new transformation methods that are both powerful for detect…

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…

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…

General measures of effect size to calculate power and sample size for Wald tests with generalized linear models
Amy L Cochran, Shijie Yuan, Paul J Rathouz
https://arxiv.org/abs/2506.22324

General measures of effect size to calculate power and sample size for Wald tests with generalized linear models
Power and sample size calculations for Wald tests in generalized linear models (GLMs) are often limited to specific cases like logistic regression. More general methods typically require detailed study parameters that are difficult to obtain during planning. We introduce two new effect size measures for estimating power, sample size, or the minimally detectable effect size in studies using Wald tests across any GLM. These measures accommodate any number of predictors or adjusters and require on…

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…

Change point detection in ERA5 ground temperature time series
Fatemeh Aghaei A., Ewan T. Phillips, Holger Kantz
https://arxiv.org/abs/2507.15045 https://…

Change point detection in ERA5 ground temperature time series
We analyze the ERA5 reanalysis 2-meter temperature time series on all land grid points using change point analysis. We fit two linear slopes to the data with the constraint that they merge at the point in time where the slope changes. We compare such fits to a standard linear regression in two ways: We use Akaike's and the Bayesian information criteria for model selection, and we test against the null hypothesis of no change of the trend value. For those grid points where the …

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…

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing
Joshua Agterberg, Ren\'e Vidal
https://arxiv.org/abs/2506.20659 https://

A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing
The problem of matrix sensing, or trace regression, is a problem wherein one wishes to estimate a low-rank matrix from linear measurements perturbed with noise. A number of existing works have studied both convex and nonconvex approaches to this problem, establishing minimax error rates when the number of measurements is sufficiently large relative to the rank and dimension of the low-rank matrix, though a precise comparison of these procedures still remains unexplored. In this work we provide …

Regression approaches for modelling genotype-environment interaction and making predictions into unseen environments
Maksym Hrachov, Hans-Peter Piepho, Niaz Md. Farhat Rahman, Waqas Ahmed Malik
https://arxiv.org/abs/2507.18125

Regression approaches for modelling genotype-environment interaction and making predictions into unseen environments
In plant breeding and variety testing, there is an increasing interest in making use of environmental information to enhance predictions for new environments. Here, we will review linear mixed models that have been proposed for this purpose. The emphasis will be on predictions and on methods to assess the uncertainty of predictions for new environments. Our point of departure is straight-line regression, which may be extended to multiple environmental covariates and genotype-specific responses.…

Sensitivity of weighted least squares estimators to omitted variables
Leonard Wainstein, Chad Hazlett
https://arxiv.org/abs/2508.02954 https://arxiv.org/pd…

Sensitivity of weighted least squares estimators to omitted variables
This paper introduces tools for assessing the sensitivity, to unobserved confounding, of a common estimator of the causal effect of a treatment on an outcome that employs weights: the weighted linear regression of the outcome on the treatment and observed covariates. We demonstrate through the omitted variable bias framework that the bias of this estimator is a function of two intuitive sensitivity parameters: (i) the proportion of weighted variance in the treatment that unobserved confounding …

Replaced article(s) found for math.ST. https://arxiv.org/list/math.ST/new
[1/1]:
- An extended latent factor framework for ill-posed linear regression
Gianluca Finocchio, Tatyana Krivobokova

A direct approach to tree-guided feature aggregation for high-dimensional regression
Jinwen Fu, Aaron J. Molstad, Hui Zou
https://arxiv.org/abs/2507.19650 https://

A direct approach to tree-guided feature aggregation for high-dimensional regression
In high-dimensional linear models, sparsity is often exploited to reduce variability and achieve parsimony. Equi-sparsity, where one assumes that predictors can be aggregated into groups sharing the same effects, is an alternative parsimonious structure that can be more suitable in certain applications. Previous work has clearly demonstrated the benefits of exploiting equi-sparsity in the presence of ``rare features'' (Yan and Bien 2021). In this work, we propose a new tree-guided regularizatio…

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…