In applied sciences, generalized linear mixed models have become one of the preferred tools to analyze a variety of longitudinal and clustered data. Due to software limitations, the analyses are often restricted to the setting in which the random effects terms follow a multivariate normal distribution. However, this assumption may be unrealistic, obscuring important features of among-unit variation. This work describes a widely applicable semiparametric Bayesian approach that relaxes the normality assumption by using a novel mixture of multivariate Polya trees prior to define a flexible nonparametric model for the random effects distribution. The nonparametric prior is centered on the commonly used parametric normal family. We allow this parametric family to hold only approximately, thereby providing a robust alternative for modeling. We discuss and implement practical procedures for addressing the computational challenges that arise under this approach. We illustrate the methodology by applying it to real-life examples. Supplemental materials for this paper are available online.

,
doi.org/10.1198/jcgs.2009.07062, hdl.handle.net/1765/32557
Journal of Computational and Graphical Statistics
Erasmus MC: University Medical Center Rotterdam

Jara, A, Hanson, T.E, & Lesaffre, E.M.E.H. (2009). Robustifying generalized linear mixed models using a new class of mixtures of multivariate Polya trees. Journal of Computational and Graphical Statistics, 18(4), 838–860. doi:10.1198/jcgs.2009.07062