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In this paper we propose a Bayesian methodology for predicting match outcomes. The methodology is illustrated on the 2006 Soccer World Cup. As prior information, we make use of the specialists' opinions and the FIFA ratings. The method is applied to calculate the win, draw and loss probabilities at each match and also to simulate the whole competition in order to estimate classification probabilities in group stage and winning tournament chances for each team. The prediction capability of the proposed methodology is determined by the DeFinetti measure and by the percentage of correct forecasts. Journal of the Operational Research Society (2010) 61, 1530-1539. doi: 10.1057/jors.2009.127 Published online 14 October 2009 Keywords: Bayesian inference; forecasting; simulation; Soccer World Cup; sports; statistics

In binary regression, symmetric links such as logit and probit are usually considered as standard. However, in the presence of unbalancing of ones and zeros, these links can be inappropriate and inflexible to fit the skewness in the response curve and likely to lead to misspecification. This is the case of covering some type of insurance, where it can be observed that the probability of a given binary response variable approaches zero at different rates than it approaches one. Furthermore, when usual links are considered, there is not a skewness parameter associated with the distribution chosen that, regardless of the linear predictor, is easily interpreted. In order to overcome such problems, a proposal for the construction of a set of new skew links is developed in this paper, where some of their properties are discussed. In this context, power links and their reversal versions are presented. A Bayesian inference approach using MCMC is developed for the presented models. The methodology is illustrated considering a sample of motor insurance policyholders selected randomly by gender. Results suggest that the proposed link functions are more appropriate than other alternative link functions commonly used in the literature. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper we introduce a Bayesian mixture model with an unknown number of components for partitioning gene expression data. Inferences about all the unknown parameters involved are made by using the proposed data-driven Markov chain Monte Carlo. This algorithm is essentially a Metropolis–Hastings within Gibbs sampling. The Metropolis–Hastings is performed to change the number of partitions k in the neighborhood and using a pair of split-merge moves. Our strategy for splitting is based on data in which allocation probabilities are calculated based on marginal likelihood function from the previously allocated observations. Conditional on k, the partitions labels are updated via Gibbs sampling. The two main advantages of the proposed algorithm is that it is easy to be implemented and the acceptance probability for split-merge movements depends only on the observed data. We examine the performance of the proposed algorithm on simulated data and then analyze two publicly available gene expression data sets. [ABSTRACT FROM AUTHOR]

In this article, we propose a bivariate long-term distribution based on the Farlie-Gumbel-Morgenstern copula model. The proposed model allows for the presence of censored data and covariates. For inferential purposes, a Bayesian approach via Markov Chain Monte Carlo (MCMC) were considered. Further, some discussions on the model selection criteria are given. In order to examine outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated on artificial and real data. [ABSTRACT FROM PUBLISHER]