Linear Mixed Models with Marginally Symmetric Nonparametric Random Effects

TL;DR

This paper introduces a marginally symmetric nonparametric maximum likelihood model for random effects in linear mixed models, reducing parameters and improving flexibility over traditional models, with an EM algorithm for estimation.

Contribution

The paper proposes the MSNPML model that assumes marginal symmetry in random effects, offering a more parsimonious alternative to NPML models with an EM algorithm for estimation.

Findings

The EM algorithm converges monotonically to a stationary point.

The ML estimator is consistent and asymptotically normal.

The model effectively estimates random-effects covariance and posterior expectations.

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 utilize 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.