Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models

Alexandre Belloni, Victor Chernozhukov and Kengo Kato

Journal of the American Statistical Association, 2019, vol. 114, issue 526, 749-758

Abstract: This work proposes new inference methods for a regression coefficient of interest in a (heterogenous) quantile regression model. We consider a high-dimensional model where the number of regressors potentially exceeds the sample size but a subset of them suffices to construct a reasonable approximation to the conditional quantile function. The proposed methods are (explicitly or implicitly) based on orthogonal score functions that protect against moderate model selection mistakes, which are often inevitable in the approximately sparse model considered in the present article. We establish the uniform validity of the proposed confidence regions for the quantile regression coefficient. Importantly, these methods directly apply to more than one variable and a continuum of quantile indices. In addition, the performance of the proposed methods is illustrated through Monte Carlo experiments and an empirical example, dealing with risk factors in childhood malnutrition. Supplementary materials for this article are available online.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2018.1442339 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models (2016)
Working Paper: Valid post-selection inference in high-dimensional approximately sparse quantile regression models (2014)
Working Paper: Valid post-selection inference in high-dimensional approximately sparse quantile regression models (2014)
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:taf:jnlasa:v:114:y:2019:i:526:p:749-758

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2018.1442339

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().