Following a Bayesian statistical inference paradigm, we provide an alternative methodology for analyzing a multivariate logistic regression. We use a multivariate normal prior in the Bayesian analysis. We present a unique Bayes estimator associated with a prior which is admissible. The Bayes estimators of the coefficients of the model are obtained via MCMC methods. The proposed procedure...

Small area estimation has received a lot of attention in recent years due to growing demand for reliable small area statistics. Traditional area-specific estimators may not provide adequate precision because sample sizes in small areas are seldom large enough. This makes it necessary to employ indirect estimators based on linking models. Basic area level and unit level models have been extensiv...

BACKGROUND Five main methods, commonly applied in genomic selection, were used to estimate the GEBV on the 15th QTLMAS workshop dataset: GBLUP, LASSO, Bayes A and two Bayes B type of methods (BBn and BBt). GBLUP is a mixed model approach where GEBV are obtained using a relationship matrix calculated from the SNP genotypes. The remaining methods are regression-based approaches where the SNP effe...

Variational Bayes (VB) has become a widely-used tool for Bayesian inference in statistics and machine learning. Nonetheless, the development of existing VB algorithms is so far generally restricted to case where variational parameter space Euclidean, which hinders potential broad application methods. This paper extends scope Riemannian manifold. We develop an efficient manifold-based algorithm ...

In this work, fundamental properties of Bayes decision rule using general loss functions are derived analytically and are verified experimentally for automatic speech recognition. It is shown that, for maximum posterior probabilities larger than 1/2, Bayes decision rule with a metric loss function always decides on the posterior maximizing class independent of the specific choice of (metric) lo...