We develop a new class of prior distributions for Bayesian comparison of nested models, which we call intrinsic moment priors, by combining the well-established notion of intrinsic prior with the recently introduced idea of non-local priors, and in particular of moment priors. Specifically, we...

Some of the methods of estimation of allele frequencies and inbreeding coefficients in a K-allele model are examined. A result that has long been assumed to be true is proved. That is, in the presence of inbreeding, the maximum likelihood estimators of the allele frequencies and of the...

In the past decade, many statistical methods have been proposed for the analysis of case-control genetic data with an emphasis on haplotype-based disease association studies. Most of the methodology has concentrated on the estimation of genetic (haplotype) main effects. Most methods accounted...

Consider a probability model P θ,α , where θ=(θ 1 ,θ 2 ,…,θ k ) T is a parameter vector of interest, and α is some nuisance parameter. The problem of testing null hypothesis H 0 : θ 1 =θ 2 =…=θ k against selecting one of k alternative hypotheses H i :θ i =θ [ k ] θ [1] , i...

Abstract For many decades, statisticians have made attempts to prepare the Bayesian omelette without breaking the Bayesian eggs; that is, to obtain probabilistic likelihood-based inferences without relying on informative prior distributions. A recent example is Murray Aitkin´s recent book,...

In this article, various issues related to the implementation of the usual Bayesian Information Criterion (BIC) are critically examined in the context of modelling a finite population. A suitable design-based approximation to the BIC is proposed in order to avoid the derivation of the exact...

Methods for Bayesian testing and assessment of dynamic quantile forecasts are proposed. Specifically, Bayes factor analogues of popular frequentist tests for independence of violations from, and for correct coverage of a time series of, quantile forecasts are developed. To evaluate the relevant...