Improved kth power expectile regression with nonignorable dropouts

Détails bibliographiques
Publié dans:	Journal of applied statistics. - 1991. - 49(2022), 11 vom: 22., Seite 2767-2788
Auteur principal:	Li, Dongyu (Auteur)
Autres auteurs:	Wang, Lei
Format:	Article en ligne
Langue:	English
Publié:	2022
Accès à la collection:	Journal of applied statistics
Sujets:	Journal Article Dropout propensity empirical likelihood expectile regression inverse probability weighting missing not at random nonresponse instrument

Description
Résumé:	© 2021 Informa UK Limited, trading as Taylor & Francis Group. The kth ( 1 < k ≤ 2 ) power expectile regression (ER) can balance robustness and effectiveness between the ordinary quantile regression and ER simultaneously. Motivated by a longitudinal ACTG 193A data with nonignorable dropouts, we propose a two-stage estimation procedure and statistical inference methods based on the kth power ER and empirical likelihood to accommodate both the within-subject correlations and nonignorable dropouts. Firstly, we construct the bias-corrected generalized estimating equations by combining the kth power ER and inverse probability weighting approaches. Subsequently, the generalized method of moments is utilized to estimate the parameters in the nonignorable dropout propensity based on sufficient instrumental estimating equations. Secondly, in order to incorporate the within-subject correlations under an informative working correlation structure, we borrow the idea of quadratic inference function to obtain the improved empirical likelihood procedures. The asymptotic properties of the corresponding estimators and their confidence regions are derived. The finite-sample performance of the proposed estimators is studied through simulation and an application to the ACTG 193A data is also presented
Description:	Date Revised 01.09.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE
ISSN:	0266-4763
DOI:	10.1080/02664763.2021.1919606