Quantile regression has become a valuable tool to analyze heterogeneous covaraite-
response associations that are often encountered in practice. The development of quantile …

Quantile regression is a powerful tool for learning the relationship between a response
variable and a multivariate predictor while exploring heterogeneous effects. This paper …

ℓ1-penalized quantile regression (ℓ1-QR) is a useful tool for modeling the relationship
between input and output variables when detecting heterogeneous effects in the high …

Abstract ℓ 1-penalized quantile regression (QR) is widely used for analysing high-
dimensional data with heterogeneity. It is now recognized that the ℓ 1-penalty introduces …

We propose a new adaptive L1 penalized quantile regression estimator for high-
dimensional sparse regression models with heterogeneous error sequences. We show that …

Hypothesis tests in models whose dimension far exceeds the sample size can be formulated
much like the classical studentized tests only after the initial bias of estimation is removed …

In this paper, we discuss a family of robust, high-dimensional regression models for quantile
and composite quantile regression, both with and without an adaptive lasso penalty for …

In this paper, we propose a frequentist model averaging method for quantile regression with
high‐dimensional covariates. Although research on these subjects has proliferated as …

Partially linear additive quantile regression in ultra-high dimension Page 1 The Annals of
Statistics 2016, Vol. 44, No. 1, 288–317 DOI: 10.1214/15-AOS1367 © Institute of …

Penalized quantile regression (QR) is widely used for studying the relationship between a
response variable and a set of predictors under data heterogeneity in high-dimensional …