Computational Statistics & Data Analysis
Sampling from the posterior distribution in generalized linear mixed models
Statistics and Computing
A Dirichlet process mixture model for the analysis of correlated binary responses
Computational Statistics & Data Analysis
Flexible random intercept models for binary outcomes using mixtures of normals
Computational Statistics & Data Analysis
Monte Carlo Statistical Methods
Monte Carlo Statistical Methods
On the asymptotic convergence of B-spline wavelets to Gabor functions
IEEE Transactions on Information Theory - Part 2
An alternative specification of generalized linear mixed models
Computational Statistics & Data Analysis
Approximate Bayesian inference in spatial GLMM with skew normal latent variables
Computational Statistics & Data Analysis
On a hybrid data cloning method and its application in generalized linear mixed models
Statistics and Computing
Computational Statistics & Data Analysis
Penalized marginal likelihood estimation of finite mixtures of Archimedean copulas
Computational Statistics
| Hi-index | 0.03 |
Generalized linear mixed models are popular for regressing a discrete response when there is clustering, e.g. in longitudinal studies or in hierarchical data structures. It is standard to assume that the random effects have a normal distribution. Recently, it has been examined whether wrongly assuming a normal distribution for the random effects is important for the estimation of the fixed effects parameters. While it has been shown that misspecifying the distribution of the random effects has a minor effect in the context of linear mixed models, the conclusion for generalized mixed models is less clear. Some studies report a minor impact, while others report that the assumption of normality really matters especially when the variance of the random effect is relatively high. Since it is unclear whether the normality assumption is truly satisfied in practice, it is important that generalized mixed models are available which relax the normality assumption. A replacement of the normal distribution with a mixture of Gaussian distributions specified on a grid whereby only the weights of the mixture components are estimated using a penalized approach ensuring a smooth distribution for the random effects is proposed. The parameters of the model are estimated in a Bayesian context using MCMC techniques. The usefulness of the approach is illustrated on two longitudinal studies using R-functions.