Overdispersion and correlation are two features often encountered when modeling non-Gaussian dependent data, usually as a function of known covariates. Methods that ignore the presence of these phenomena are often in jeopardy of leading to biased assessment of covariate effects. The...

The Northern Manhattan Study (NOMAS) is a prospective, population-based study. One of the goals of NOMAS is to characterize the functional status of stroke survivors over time after stroke. Based on generalized estimating equation models, previous parametric analysis showed that functional...

Adaptive seamless phase II/III clinical trial designs allowing treatment selection at an interim analysis have gained much attention because of their potential benefits compared to more conventional drug development programmes with separate trials for individual phases. A scenario of particular...

Data integration is becoming an essential tool to cope with and make sense of the ever increasing amount of biological data. Genomic data arises in various shapes and forms including vectors, graphs or sequences, therefore, it is essential to carefully consider strategies that best capture the...

This paper uses Bayesian tree models for statistical benchmarking in data sets with awkward marginals and complicated dependence structures. The method is applied to a very large database on corporate performance over the last four decades. The results of this study provide a formal basis for...

The construction of a multivariate two sample test is considered. An attractive approach to this problem, for instance when the data contain missing values or the number of variables is large, is to form an overall test by combining the componentwise test statistics. This can be done via their...

As an extension of multi-class classification, machine learning algorithms have been proposed that are able to deal with situations in which the class labels are defined in a non-crisp way. Objects exhibit in that sense a degree of membership to several classes. In a similar setting, models are...

The well known latent variable representation of the Bayesian probit regression model due to Albert and Chib (1993) allows model fitting to be performed using a simple Gibbs sampler. In addition, various types of dependence among categorical outcomes not explained by covariate information can be...

Analytical results for reducing the parameter space dimension when computing the marginal likelihood are given for the broad class of dynamic mixture models. These results allow the integration of scale parameters out of the likelihood by Kalman filtering and Gaussian quadrature. The method is...

The proportion ratio (PR) of patient response is one of the most commonly used indices for measuring the relative treatment effect in a randomized clinical trial (RCT). Assuming a random effect multiplicative risk model, we develop two point estimators and three interval estimators in closed...