Modeling of covariance structures of random effects and random errors in linearmixed models

Yu Fei, Yating Pan, Yin Chen, Jianxin Pan

    Research output: Contribution to journalArticlepeer-review

    Abstract

    In this paper, we discuss how to model the mean and covariance structures in linear mixed models (LMMs), simultaneously. We propose a data-driven method to model covariance structures of the random effects and random errors in the LMMs. Parameter estimation in the mean and covariances is considered by using EM algorithm, and standard errors of the parameter estimates are calculated through Louis’ (1982) information principle. Kenward’s (1987) cattle data sets are analyzed for illustration, and comparison to the literature work is made through simulation
    studies. Our numerical analysis confirms the superiority of the proposed method to existing approaches in terms of Akaike information criterion.
    Original languageEnglish
    Pages (from-to)2748-2769
    Number of pages22
    JournalCommunications in Statistics - Theory and Methods
    Volume45
    Issue number9
    Publication statusPublished - 28 Apr 2016

    Keywords

    • Covariance modeling
    • Expectation-Maximization (EM) algorithm
    • Linear mixed models
    • Longitudinal data

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