Multivariate Logistic Mixtures

Bayesian multivariate logistic regression.

Bayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual outcomes. In addition, difficulties arise when simple noninformative priors are chosen for the covariance parameters. Motivated by these problems, we propose a new type of multivariate logistic dis...

Logistic Mixtures of Generalized Linear

Title of Dissertation LOGISTIC MIXTURES OF GENERALIZED LINEAR MODEL TIMES SERIES Neal Overton Je ries Doctor of Philosophy Dissertation directed by Professor Benjamin Kedem Mathematics Department Statistics Program In this dissertation we propose a class of time series models for mixture data We call these logistic mixtures In such models the mixture s component densi ties have a generalized li...

Tutorial on Multivariate Logistic Regression

Multivariate Kernel Partition Process Mixtures.

Mixtures provide a useful approach for relaxing parametric assumptions. Discrete mixture models induce clusters, typically with the same cluster allocation for each parameter in multivariate cases. As a more flexible approach that facilitates sparse nonparametric modeling of multivariate random effects distributions, this article proposes a kernel partition process (KPP) in which the cluster al...

Inference for multivariate normal mixtures

Multivariate normal mixtures provide a flexible model for high-dimensional data. They are widely used in statistical genetics, statistical finance, and other disciplines. Due to the unboundedness of the likelihood function, classical likelihoodbased methods, which may have nice practical properties, are inconsistent. In this paper, we recommend a penalized likelihood method for estimating the m...