Empirical‐likelihood‐based criteria for model selection on marginal analysis of longitudinal data with dropout missingness

DOI10.1111/biom.13060zbMath1436.62522arXiv1804.07430OpenAlexW3106314514WikidataQ90028114 ScholiaQ90028114MaRDI QIDQ5214571

Yuan Xue, Biyi Shen, Chixiang Chen, Ming Wang, Li-jun Zhang

Publication date: 7 February 2020

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1804.07430

zbMATH Keywords

longitudinal datamodel selectionAkaike information criterionBayesian information criterionmissing at randomempirical likelihoodweighted generalized estimating equation

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Applications of statistics to biology and medical sciences; meta analysis (62P10) Statistical aspects of information-theoretic topics (62B10) Missing data (62D10)

Related Items (4)

A multiple robust propensity score method for longitudinal analysis with intermittent missing data ⋮ Empirical likelihood based tests for detecting the presence of significant predictors in marginal quantile regression ⋮ Synthesizing secondary data into survival analysis to improve estimation efficiency ⋮ A Robust Consistent Information Criterion for Model Selection Based on Empirical Likelihood

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