Cover Image

The $\ell$-test: leveraging sparsity in the Gaussian linear model for improved inference

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sengupta, Souhardya, Janson, Lucas
Format:	Preprint
Published:	2024
Subjects:	Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

The $L$-test: Increasing the Linear Model $F$-test's Power Under Sparsity Without Sacrificing Validity
by: Paulson, Danielle, et al.
Published: (2025)

Low-rank Covariate Balancing Estimators under Interference
by: Sengupta, Souhardya, et al.
Published: (2025)

Bayesian inference of sparsity in stable vector autoregressive processes
by: Heaps, Sarah E., et al.
Published: (2026)

Conditioning on posterior samples for flexible frequentist goodness-of-fit testing
by: Bhaduri, Ritwik, et al.
Published: (2025)

Semiparametric Inference for Partially Identifiable Data Fusion Estimands via Double Machine Learning
by: Jiang, Yicong, et al.
Published: (2025)

A modelling framework for detecting and leveraging node-level information in Bayesian network inference
by: Xi, Xiaoyue, et al.
Published: (2023)

Statistical inference for core-periphery structures
by: Yanchenko, Eric, et al.
Published: (2025)

Powerful Partial Conjunction Hypothesis Testing via Conditioning
by: Liang, Biyonka, et al.
Published: (2022)

Uniform inference in linear mixed models
by: Ekvall, Karl Oskar, et al.
Published: (2025)

Scalable skewed Bayesian inference for latent Gaussian models
by: Dutta, Shourya, et al.
Published: (2025)

An adaptive variance estimator for relative sparsity
by: Weisenthal, Samuel Julian
Published: (2026)

Inference for relative sparsity
by: Weisenthal, Samuel J., et al.
Published: (2023)

Fast and scalable inference in hidden Markov models with Gaussian fields
by: Fischer, Jan-Ole
Published: (2026)

PICS: A sequential approach to obtain optimal designs for non-linear models leveraging closed-form solutions for faster convergence
by: Ghosh, Suvrojit, et al.
Published: (2024)

Post-selection inference in generalized linear models via parametric programming
by: Shen, Qinyan, et al.
Published: (2026)

Group selection and shrinkage: Structured sparsity for semiparametric additive models
by: Thompson, Ryan, et al.
Published: (2021)

Predictive variational inference for flexible regression models
by: Kock, Lucas, et al.
Published: (2026)

Compositional Covariate Importance Testing via Partial Conjunction of Bivariate Hypotheses
by: Bhaduri, Ritwik, et al.
Published: (2024)

Chiseling: Powerful and Valid Subgroup Selection via Interactive Machine Learning
by: Cheng, Nathan, et al.
Published: (2025)

Statistical inference for partially shape-constrained function-on-scalar linear regression models
by: Han, Kyunghee, et al.
Published: (2024)

Estimation and inference of high-dimensional partially linear regression models with latent factors
by: Shi, Yanmei, et al.
Published: (2025)

Metropolized Knockoff Sampling
by: Bates, Stephen, et al.
Published: (2019)

Fast, effective, and coherent time series modeling using the sparsity-ranked lasso
by: Peterson, Ryan, et al.
Published: (2022)

Repulsive mixtures via the sparsity-inducing partition prior
by: Mozdzen, Alexander, et al.
Published: (2025)

Robust distribution-free tests for the linear model
by: Hilbert, Torey, et al.
Published: (2024)

Robust inference for linear regression models with possibly skewed error distribution
by: Nandy, Amarnath, et al.
Published: (2024)

Bayesian inference for scale mixtures of skew-normal linear models under the centered parameterization
by: de Freitas, João Victor B., et al.
Published: (2024)

Goodness-of-fit testing for the error distribution in functional linear models
by: Neumeyer, Natalie, et al.
Published: (2025)

Simultaneous inference for generalized linear models with unmeasured confounders
by: Du, Jin-Hong, et al.
Published: (2023)

Variational inference for hierarchical models with conditional scale and skewness corrections
by: Kock, Lucas, et al.
Published: (2025)

Fast Bayesian inference in a class of sparse linear mixed effects models
by: Spyropoulou, M-Z., et al.
Published: (2024)

Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis
by: Ham, Dae Woong, et al.
Published: (2022)

Empirical Bayes inference in sparse high-dimensional generalized linear models
by: Tang, Yiqi, et al.
Published: (2023)

A tree-based model for addressing sparsity and taxa covariance in microbiome compositional count data
by: Wang, Zhuoqun, et al.
Published: (2021)

General linear hypothesis testing in ill-conditioned functional response model
by: Smaga, Łukasz, et al.
Published: (2024)

Safe hypotheses testing with application to order restricted inference
by: Davidov, Ori
Published: (2026)

Familial inference: tests for hypotheses on a family of centres
by: Thompson, Ryan, et al.
Published: (2022)

A generalized hypothesis test for community structure in networks
by: Yanchenko, Eric, et al.
Published: (2021)

Double Robust high dimensional alpha test for linear factor pricing model
by: Zhao, Ping, et al.
Published: (2024)

Scalable Bayesian inference on high-dimensional multivariate linear regression
by: Cao, Xuan, et al.
Published: (2025)