Moving Beyond Sub-Gaussianity in High-Dimensional Statistics: Applications in Covariance Estimation and Linear Regression

8 April 2018

Papers citing "Moving Beyond Sub-Gaussianity in High-Dimensional Statistics: Applications in Covariance Estimation and Linear Regression"

22 / 22 papers shown

Title
Weighted Random Dot Product Graphs Bernardo Marenco P. Bermolen Marcelo Fiori Federico Larroca Gonzalo Mateos 50 1 0 06 May 2025
Concentration Inequalities for Statistical Inference Huiming Zhang Songxi Chen 53 63 0 24 Feb 2025
Exponential tilting of subweibull distributions F. W. Townes 36 1 0 16 Jul 2024
On the best approximation by finite Gaussian mixtures Yun Ma Yihong Wu Pengkun Yang 43 1 0 13 Apr 2024
General Tail Bounds for Non-Smooth Stochastic Mirror Descent Khaled Eldowa Andrea Paudice 26 5 0 12 Dec 2023
A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks Behrad Moniri Donghwan Lee Hamed Hassani Yan Sun MLT 45 19 0 11 Oct 2023
Estimation of sparse linear regression coefficients under $L$ -subexponential covariates Takeyuki Sasai 31 0 0 24 Apr 2023
Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review M. Barigozzi 13 2 0 21 Mar 2023
Tight Concentration Inequality for Sub-Weibull Random Variables with Generalized Bernstien Orlicz norm Heejong Bong Arun K. Kuchibhotla 11 2 0 08 Feb 2023
Near Optimal Private and Robust Linear Regression Xiyang Liu Prateek Jain Weihao Kong Sewoong Oh A. Suggala 41 9 0 30 Jan 2023
Quantizing Heavy-tailed Data in Statistical Estimation: (Near) Minimax Rates, Covariate Quantization, and Uniform Recovery Junren Chen Michael Kwok-Po Ng Di Wang MQ 28 13 0 30 Dec 2022
Testing Many Constraints in Possibly Irregular Models Using Incomplete U-Statistics Nils Sturma Mathias Drton Dennis Leung 30 3 0 24 Aug 2022
Nonconvex Stochastic Scaled-Gradient Descent and Generalized Eigenvector Problems C. J. Li Michael I. Jordan 37 2 0 29 Dec 2021
Bayesian neural network unit priors and generalized Weibull-tail property M. Vladimirova Julyan Arbel Stéphane Girard BDL 54 9 0 06 Oct 2021
Reinforcement learning for linear-convex models with jumps via stability analysis of feedback controls Xin Guo Anran Hu Yufei Zhang 24 24 0 19 Apr 2021
Outlier-Robust Learning of Ising Models Under Dobrushin's Condition Ilias Diakonikolas D. Kane Alistair Stewart Yuxin Sun 24 15 0 03 Feb 2021
$Central Limit Theorem and Bootstrap Approximation in High Dimensions: Near $1/\sqrt{n}$ Rates via Implicit Smoothing$ Central Limit Theorem and Bootstrap Approximation in High Dimensions: Near $1/\sqrt{n}$ Rates via Implicit Smoothing Miles E. Lopes 24 20 0 13 Sep 2020
On the Non-Asymptotic Concentration of Heteroskedastic Wishart-type Matrix T. Tony Cai Rungang Han Anru R. Zhang 42 15 0 28 Aug 2020
Convergence rate of estimators of clustered panel models with misclassification Andreas Dzemski R. Okui 8 4 0 11 Aug 2020
Rejoinder: On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning Lin Liu Rajarshi Mukherjee J. M. Robins CML 35 16 0 07 Aug 2020
Sharp Concentration Results for Heavy-Tailed Distributions Milad Bakhshizadeh A. Maleki Víctor Pena 17 23 0 30 Mar 2020
Method of Contraction-Expansion (MOCE) for Simultaneous Inference in Linear Models Fei Wang Ling Zhou Lu Tang P. Song 25 4 0 04 Aug 2019