Tootfinder

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.

hdbayes: An R Package for Bayesian Analysis of Generalized Linear Models Using Historical Data
Ethan M. Alt, Xinxin Chen, Luiz M. Carvalho, Joseph G. Ibrahim
https://arxiv.org/abs/2506.20060

hdbayes: An R Package for Bayesian Analysis of Generalized Linear Models Using Historical Data
There has been increased interest in the use of historical data to formulate informative priors in regression models. While many such priors for incorporating historical data have been proposed, adoption is limited due to access to software. Where software does exist, the implementations between different methods could be vastly different, making comparisons between methods difficult. In this paper, we introduce the R package hdbayes, an implementation of the power prior, normalized power prior…

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 …

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 …

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 …

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 …

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…

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…

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…

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…

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…

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…

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 …

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…

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…

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

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-…

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…

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…

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…

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…

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.…

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…

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…

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…