Image de couverture de livre

Bayesian iterative screening in ultra-high dimensional linear regressions

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Wang, Run, Dutta, Somak, Roy, Vivekananda
Format:	Preprint
Publié:	2021
Sujets:	Methodology
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Ridge partial correlation screening for ultrahigh-dimensional data
par: Wang, Run, et autres
Publié: (2025)

Scalable Bayesian inference on high-dimensional multivariate linear regression
par: Cao, Xuan, et autres
Publié: (2025)

Necessary and sufficient conditions for posterior propriety for generalized linear mixed models
par: Rao, Yalin, et autres
Publié: (2023)

Conditional variable screening for ultra-high dimensional longitudinal data with time interactions
par: Bratsberg, Andrea, et autres
Publié: (2023)

A geometric approach to informed MCMC sampling
par: Roy, Vivekananda
Publié: (2024)

Valid F-screening in linear regression
par: McGough, Olivia, et autres
Publié: (2025)

Estimation and inference of high-dimensional partially linear regression models with latent factors
par: Shi, Yanmei, et autres
Publié: (2025)

Testing relevant difference in high-dimensional linear regression with applications to detect transferability
par: Liu, Xu
Publié: (2025)

Rényi's $α$-divergence variational Bayes for spike-and-slab high-dimensional linear regression
par: Bsila, Chadi, et autres
Publié: (2025)

Comparison study of variable selection procedures in high-dimensional Gaussian linear regression
par: Lacroix, Perrine, et autres
Publié: (2021)

Detection and inference of changes in high-dimensional linear regression with non-sparse structures
par: Cho, Haeran, et autres
Publié: (2024)

Decorrelated forward regression for high dimensional data analysis
par: Jiang, Xuejun, et autres
Publié: (2024)

An efficient tensor regression for high-dimensional data
par: Si, Yuefeng, et autres
Publié: (2022)

Nonparametric regression of spatio-temporal data using infinite-dimensional covariates
par: Roy, Subhrajyoty, et autres
Publié: (2026)

Fitting sparse high-dimensional varying-coefficient models with Bayesian regression tree ensembles
par: Ghosh, Soham, et autres
Publié: (2025)

Quantifying predictive uncertainty of aphasia severity in stroke patients with sparse heteroscedastic Bayesian high-dimensional regression
par: Zgodic, Anja, et autres
Publié: (2023)

Semiparametric model averaging for high-dimensional quantile regression with nonignorable nonresponse
par: Xiong, Wei, et autres
Publié: (2025)

Statistical inference for high-dimensional convoluted rank regression
par: Cai, Leheng, et autres
Publié: (2024)

The data augmentation algorithm
par: Roy, Vivekananda, et autres
Publié: (2024)

Bayesian High-dimensional Grouped-regression using Sparse Projection-posterior
par: Pal, Samhita, et autres
Publié: (2024)

Scalable approximation of the transformation-free linear simplicial-simplicial regression via constrained iterative reweighted least squares
par: Tsagris, Michail, et autres
Publié: (2025)

Semi-supervised linear regression: enhancing efficiency and robustness in high dimensions
par: Chen, Kai, et autres
Publié: (2023)

Contrastive linear regression
par: Zhang, Boyang, et autres
Publié: (2024)

Robust and consistent model evaluation criteria in high-dimensional regression
par: Kurata, Sumito, et autres
Publié: (2024)

Rank-based Maxsum test for high dimensional regression coefficient
par: Zhao, Ping, et autres
Publié: (2026)

Dimension-free bounds in high-dimensional linear regression via error-in-operator approach
par: Noskov, Fedor, et autres
Publié: (2025)

A generalized Bayesian approach for high-dimensional robust regression with serially correlated errors and predictors
par: Chakraborty, Saptarshi, et autres
Publié: (2024)

Large multi-response linear regression estimation based on low-rank pre-smoothing
par: Tian, Xinle, et autres
Publié: (2024)

Inference of high-dimensional weak instrumental variable regression models without ridge-regularization
par: Ding, Jiarong, et autres
Publié: (2025)

A covariate-dependent Cholesky decomposition for high-dimensional covariance regression
par: Kim, Rakheon, et autres
Publié: (2026)

Adaptive adequacy testing of high-dimensional factor-augmented regression model
par: Shi, Yanmei, et autres
Publié: (2025)

Efficient posterior sampling for high-dimensional imbalanced logistic regression
par: Sen, Deborshee, et autres
Publié: (2019)

Pseudo-R2D2 prior for high-dimensional ordinal regression
par: Yanchenko, Eric
Publié: (2025)

A direct approach to tree-guided feature aggregation for high-dimensional regression
par: Fu, Jinwen, et autres
Publié: (2025)

A lack-of-fit test for quantile regression models with high-dimensional covariates
par: Conde-Amboage, Mercedes, et autres
Publié: (2015)

Inference for high-dimensional linear expectile regression with de-biased method
par: Li, Xiang, et autres
Publié: (2024)

Debiased high-dimensional regression calibration for errors-in-variables log-contrast models
par: Zhao, Huali, et autres
Publié: (2024)

Double Robust high dimensional alpha test for linear factor pricing model
par: Zhao, Ping, et autres
Publié: (2024)

Piece-wise linear isotonic regression
par: Kuosmanen, Timo, et autres
Publié: (2026)

Debiased regression adjustment in completely randomized experiments with moderately high-dimensional covariates
par: Lu, Xin, et autres
Publié: (2023)