1 citations · 1 across the 5 of their papers we have counts for
5 papers
Bayesian Smoothed Quantile Regression
Bingqi Liu, Kangqiang Li, Tianxiao Pang
The standard asymmetric Laplace framework for Bayesian quantile regression (BQR) suffers from a fundamental decision-theoretic misalignment, yielding biased finite-sample estimates…
Two Results on Low-Rank Heavy-Tailed Multiresponse Regressions
Kangqiang Li, Yuxuan Wang
This paper gives two theoretical results on estimating low-rank parameter matrices for linear models with multivariate responses. We first focus on robust parameter estimation of l…
Robust low-rank tensor regression via clipping and Huber loss
Kangqiang Li, Bingqi Liu, Yang Yang +1
In this paper, we construct a parameter estimation framework for robust low-rank tensor regression based on a truncation method and Huber loss, specifically focusing on models with…
Robust parameter estimation of regression model under weakened moment assumptions
Kangqiang Li, Songqiao Tang, Lixin Zhang
This paper provides some extended results on estimating parameter matrix of several regression models when the covariate or response possesses weaker moment condition. We study the…
Robust covariance estimation for distributed principal component analysis
Kangqiang Li, Han Bao, Lixin Zhang
Fan et al. [ (6) (2019) 3009-3031] constructed a distributed principal component analysis (PCA) algorithm to reduc…