A Bayesian hierarchical model for simultaneously estimating and partitioning probability density functions is presented. Individual density functions are flexibly modeled using Bernstein densities, which are mixtures of beta densities whose parameters depend only on the number of mixture...

In this thesis, we address issues of model estimation for longitudinal categorical data and of model selection for these data with missing covariates. Longitudinal survey data capture the responses of each subject repeatedly through time, allowing for the separation of variation in the measured...

This article devises a Bayesian multivariate formulation for analysis of ordinal data that records teacher classroom performance along multiple dimensions to assess aspects characterizing good instruction. Study designs for scoring teachers seek to measure instructional performance over multiple...

We introduce a Bayesian hierarchical regression model that extends the traditional least-squares regression model used to estimate gravity or spatial interaction relations involving origin-destination flows. Spatial interaction models attempt to explain variation in flows from n origin regions...

Malaria is a parasitic infectious tropical disease that causes high mortality rates in the tropical belt. In Venezuela, Sucre state is considered the third state with most disease prevalence. This paper presents a hierarchical regression log-Poisson space-time model within a Bayesian approach to...

In diesem Aufsatz beschreiben wir einen neuartigen Ansatz, mit dem zwei heterogene Datensätze für räumliche Hauspreisvorhersagen nutzbar gemacht werden. Der erste Datensatz enthält eine große Anzahl von Hauspreisbeobachtungen, allerdings nur wenige Immobilieneigenschaften (ein langer...

The problem of modelling football data has become increasingly popular in the last few years and many different models have been proposed with the aim of estimating the characteristics that bring a team to lose or win a game, or to predict the score of a particular match. We propose a Bayesian...