Most of the Bayesian nonparametric models for non-exchangeable data that are used in applications are based on some extension to the multivariate setting of the Dirichlet process, the best known being MacEachern’s dependent Dirichlet process. A comparison of two recently introduced classes of...

The dependence structure between WTI and Brent crude oil spot log-returns is analysed using modern copula techniques. In a first step, to account for autocorrelation and volatility clustering in the marginals, several single equation models are applied. Second, to select both copulas and tail...

In a follow-up study the time-to-event may be censored either because of dropouts or the end of study is earlier. This situation is frequently modeled through a Cox-proportional hazards model including covariates, some of which are under the control of the experimenter. When the model is to be...

Linear mixed models are especially useful when observations are grouped. In a high dimensional setting however, selecting the fixed effect coefficients in these models is mandatory as classical tools are not performing well. By considering the random effects as missing values in the linear mixed...

The effect of the prior distribution of the outcome and exposure models’ covariate inclusion indicators in the Bayesian Adjustment for Confounding (BAC) algorithm is studied. The investigative approach is to analytically describe the posterior probabilities of the outcome models in terms of...

An abundance of high-dimensional data has meant that L1 penalized regression, known as the lasso, has become an indispensable tool of the practitioner. A feature of the lasso is a “tuning” parameter that controls the amount of shrinkage applied to the coefficients. In practice, a value for...

Evaluation of candidate surrogate endpoints using individual patient data from multiple clinical trials is considered the gold standard approach to validate surrogates at both patient and trial levels. However, this approach assumes the availability of patient-level data from a relatively large...

The analysis of short longitudinal series of circular data may be problematic and to some extent has not been fully developed. A Bayesian analysis of a new model for such data is presented. The model is based on a radial projection onto the circle of a particular bivariate normal distribution....

The usefulness of a predictor evaluation framework which combines a blocked cross-validation scheme with directional accuracy measures is investigated. The advantage of using a blocked cross-validation scheme with respect to the standard out-of-sample procedure is that cross-validation yields...