Linear mixed models with marginally symmetric nonparametric random effects

Nguyen, Hien D. and McLachlan, Geoffrey J. (2016) Linear mixed models with marginally symmetric nonparametric random effects. Computational Statistics and Data Analysis, 103 151-169. doi:10.1016/j.csda.2016.05.005


Author Nguyen, Hien D.
McLachlan, Geoffrey J.
Title Linear mixed models with marginally symmetric nonparametric random effects
Journal name Computational Statistics and Data Analysis   Check publisher's open access policy
ISSN 0167-9473
1872-7352
Publication date 2016-11
Sub-type Article (original research)
DOI 10.1016/j.csda.2016.05.005
Open Access Status Not Open Access
Volume 103
Start page 151
End page 169
Total pages 19
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Collection year 2017
Language eng
Formatted abstract
Linear mixed models (LMMs) are used as an important tool in the data analysis of repeated measures and longitudinal studies. The most common form of LMMs utilizes a normal distribution to model the random effects. Such assumptions can often lead to misspecification errors when the random effects are not normal. One approach to remedy the misspecification errors is to utilize a point-mass distribution to model the random effects; this is known as the nonparametric maximum likelihood-fitted (NPML) model. The NPML model is flexible but requires a large number of parameters to characterize the random-effects distribution. It is often natural to assume that the random-effects distribution be at least marginally symmetric. The marginally symmetric NPML (MSNPML) random-effects model is introduced, which assumes a marginally symmetric point-mass distribution for the random effects. Under the symmetry assumption, the MSNPML model utilizes half the number of parameters to characterize the same number of point masses as the NPML model; thus the model confers an advantage in economy and parsimony. An EM-type algorithm is presented for the maximum likelihood (ML) estimation of LMMs with MSNPML random effects; the algorithm is shown to monotonically increase the log-likelihood and is proven to be convergent to a stationary point of the log-likelihood function in the case of convergence. Furthermore, it is shown that the ML estimator is consistent and asymptotically normal under certain conditions, and the estimation of quantities such as the random-effects covariance matrix and individual a posteriori expectations is demonstrated. A simulation study is used to illustrate the gains in efficiency of the MSNPML model over the NPML model under the assumption of symmetry. A pair of real data applications are then used to demonstrate the manner in which the MSNPML model can be used to draw useful statistical inference.
Keyword Linear mixed models
Nonparametric maximum likelihood
Marginal symmetry
Random effects
Mixture model
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
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Created: Tue, 21 Jun 2016, 13:01:13 EST by Professor Geoff Mclachlan on behalf of Mathematics