Discussion on "Predictively consistent prior effective sample sizes," by Beat Neuenschwander, Sebastian Weber, Heinz Schmidli, and Anthony O'Hagan.

We extend the approach of finding effective sample size for a typical phase II clinical trial having efficacy and toxicity as two components of the response vector. The case of binary efficacy and binary toxicity is illustrated under Dirichlet and multivariate T priors.

Localization of shapes using statistical models and stochastic optimization.

In this paper, we present a new model for deformations of shapes. A pseudo-likelihood is based on the statistical distribution of the gradient vector field of the gray level. The prior distribution is based on the Probabilistic Principal Component Analysis (PPCA).

Fusion of Hidden Markov Random Field models and its Bayesian estimation.

In this paper, we present a Hidden Markov Random Field (HMRF) data-fusion model. The proposed model is applied to the segmentation of natural images based on the fusion of colors and textons into Julesz ensembles. The corresponding Exploration/ Selection/Estimation (ESE) procedure for the estimation of the parameters is presented.

Estimation of time-dependent rate ratios in case-control studies: comparison of two approaches for exposure assessment.

Purpose: In pharmacoepidemiology, it is well recognized that the rate of adverse events may vary as a function of the cumulative duration of the drug exposure and/or the time since the end of the exposure. In case-control studies, two different approaches have been used to estimate temporal effects of drug exposure: the time-windows (T-Ws) approach and the duration-specific (D-S) approach. We decided to conduct a simulation study to compare the two approaches when the rate ratios (RRs) vary as a function of the cumulative duration of exposure and/or the time since the end of exposure.

A stochastic method for Bayesian estimation of hidden Markov random field models with application to a color model.

We propose a new stochastic algorithm for computing useful Bayesian estimators of hidden Markov random field (HMRF) models that we call exploration/selection/estimation (ESE) procedure. The algorithm is based on an optimization algorithm of O. François, called the exploration/selection (E/S) algorithm.

A Bayesian analysis of the 4-year follow-up data of the Wisconsin epidemiologic study of diabetic retinopathy.

The Wisconsin epidemiologic study of diabetic retinopathy (WESDR) is a population-based epidemiologic study carried out in Southern Wisconsin during the 1980s of the last century. The resulting data were analysed by different statisticians and ophthalmologists during the last two decades. Most of the analyses were carried out on the baseline data, although there were two follow-up studies on the same population.