Penalized empirical likelihood for longitudinal expectile regression with growing dimensional data

Expectile regression (ER) naturally extends the classical least squares to investigate heterogeneous effects of covariates on the distribution of the response variable. In this paper, we propose a penalized empirical likelihood (PEL) based ER estimator, which incorporates quadratic inference functio...

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Bibliographic Details
Published inJournal of the Korean Statistical Society Vol. 53; no. 3; pp. 752 - 773
Main Authors Zhang, Ting, Wang, Yanan, Wang, Lei
Format Journal Article
LanguageEnglish
Published Singapore Springer Nature Singapore 01.09.2024
Subjects
Applied Statistics
Bayesian Inference
Mathematics and Statistics
Research Article
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
Quadratic inference function
Within-subject correlation
Oracle property
Selection consistency
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Summary:Expectile regression (ER) naturally extends the classical least squares to investigate heterogeneous effects of covariates on the distribution of the response variable. In this paper, we propose a penalized empirical likelihood (PEL) based ER estimator, which incorporates quadratic inference function and generalized estimating equation to construct the PEL procedure for longitudinal data. We investigate the asymptotic properties of the PEL estimator when the number of covariates is allowed to diverge as the sample size increases. The finite-sample performance of the proposed estimator is studied through simulations, and an application to yeast cell-cycle gene expression data is also presented.
ISSN:1226-3192
2005-2863
DOI:10.1007/s42952-024-00265-4