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This contribution deals with effect measures for covariates in ordinal data models to address the interpretation of the results on the extreme categories of the scales, evaluate possible response styles, and motivate collapsing of extreme categories. It provides a simpler interpretation of the influence of the covariates on the probability of the response categories both in standard cumulative link models under the proportional odds assumption and in the recent extension of the C ombination of U ncertainty and P reference of the respondents models, the mixture models introduced to account for uncertainty in rating systems. The article shows by means of marginal effect measures that the effects of the covariates are underestimated when the uncertainty component is neglected. Visualization tools for the effect of covariates are proposed, and measures of relative size and partial effect based on rates of change are evaluated by the use of real data sets. [ABSTRACT FROM AUTHOR]

The authors present a Bayesian perspective on graphical marginal log-linear models. They cite five properties that contribute to the increasing attention for novel parameterizations. They examine the saturated model and the two previous models using Dirichlet priors. They also describe a way to estimate the posterior distributions of any marginal long-linear (MLL) parameterization using a simple Monte Carlo scheme.

We propose a novel Bayesian optimization procedure for outlier detection in the Capital Asset Pricing Model. We use a parametric product partition model to robustly estimate the systematic risk of an asset. We assume that the returns follow independent normal distributions and we impose a partition structure on the parameters of interest. The partition structure imposed on the parameters induces a corresponding clustering of the returns. We identify via an optimization procedure the partition that best separates standard observations from the atypical ones. The methodology is illustrated with reference to a real dataset, for which we also provide a microeconomic interpretation of the detected outliers. [ABSTRACT FROM AUTHOR]

Compares two alternative Markov Chain Monte Carlo (MCMC) samplers for the Bayesian analysis of discrete graphical models. Presentation of a hierarchical and nonhierarchical version of MCMC model; Evaluation of logistic models for large contingency tables.