Statistics and Computing

, Volume 22, Issue 1, pp 79–92

Multivariate generalized linear mixed models with semi-nonparametric and smooth nonparametric random effects densities

Article

DOI: 10.1007/s11222-010-9207-y

Cite this article as:
Papageorgiou, G. & Hinde, J. Stat Comput (2012) 22: 79. doi:10.1007/s11222-010-9207-y

Abstract

We extend the family of multivariate generalized linear mixed models to include random effects that are generated by smooth densities. We consider two such families of densities, the so-called semi-nonparametric (SNP) and smooth nonparametric (SMNP) densities. Maximum likelihood estimation, under either the SNP or the SMNP densities, is carried out using a Monte Carlo EM algorithm. This algorithm uses rejection sampling and automatically increases the MC sample size as it approaches convergence. In a simulation study we investigate the performance of these two densities in capturing the true underlying shape of the random effects distribution. We also examine the implications of misspecification of the random effects distribution on the estimation of the fixed effects and their standard errors. The impact of the assumed random effects density on the estimation of the random effects themselves is investigated in a simulation study and also in an application to a real data set.

Keywords

Longitudinal dataMixed modelsMultinomial responsesRandom effectsSemi-nonparametric densitiesSmooth nonparametric densities

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.School of Mathematics, Statistics and Applied MathematicsNational University of IrelandGalwayIreland