Post-selection Inference of High-dimensional Logistic Regression Under Case–Control Design
Yuanyuan Lin,
Jinhan Xie,
Ruijian Han and
Niansheng Tang
Journal of Business & Economic Statistics, 2023, vol. 41, issue 2, 624-635
Abstract:
Confidence sets are of key importance in high-dimensional statistical inference. Under case–control study, a popular response-selective sampling design in medical study or econometrics, we consider the confidence intervals and statistical tests for single or low-dimensional parameters in high-dimensional logistic regression model. The asymptotic properties of the resulting estimators are established under mild conditions. We also study statistical tests for testing more general and complex hypotheses of the high-dimensional parameters. The general testing procedures are proved to be asymptotically exact and have satisfactory power. Numerical studies including extensive simulations and a real data example confirm that the proposed method performs well in practical settings.
Date: 2023
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2022.2050245 (text/html)
Access to full text is restricted to subscribers.
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:taf:jnlbes:v:41:y:2023:i:2:p:624-635
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20
DOI: 10.1080/07350015.2022.2050245
Access Statistics for this article
Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan
More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().