A tuning-free efficient test for marginal linear effects in high-dimensional quantile regression

Kai Xu () and Nan An ()
Additional contact information
Kai Xu: Anhui Normal University
Nan An: Anhui Normal University

Annals of the Institute of Statistical Mathematics, 2024, vol. 76, issue 1, No 7, 93-110

Abstract: Abstract This work is concerned with testing the marginal linear effects of high-dimensional predictors in quantile regression. We introduce a novel test that is constructed using maxima of pairwise quantile correlations, which permit consistent assessment of the marginal linear effects. The proposed testing procedure is computationally efficient with the aid of a simple multiplier bootstrap method and does not involve any need to select tuning parameters, apart from the number of bootstrap replications. Other distinguishing features of the new procedure are that it imposes no structural assumptions on the unknown dependence structures of the predictor vector and allows the dimension of the predictor vector to be exponentially larger than sample size. To broaden the applicability, we further extend the preceding analysis to the censored response case. The effectiveness of our proposed approach in the finite samples is illustrated through simulation studies.

Keywords: High dimension; Marginal quantile regression; Multiplier bootstrap; Quantile correlation; Quantile slope; Randomly censored data (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10463-023-00877-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:aistmt:v:76:y:2024:i:1:d:10.1007_s10463-023-00877-3

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10463/PS2

DOI: 10.1007/s10463-023-00877-3

Access Statistics for this article

Annals of the Institute of Statistical Mathematics is currently edited by Tomoyuki Higuchi

More articles in Annals of the Institute of Statistical Mathematics from Springer, The Institute of Statistical Mathematics
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().