a two-factor experiment with interaction between factors wherein observations follow an inverse gaussian model is considered. analysis of the experiment is approached via an empirical bayes procedure. the conjugate family of prior distributions is considered. bayes and empirical bayes estimators are derived. application of the procedure is illustrated on a data set, which has previously been an...

to compare different qtl mapping methods, a population with genotypic and phenotypic data was simulated. in bayesian approach, all information of markers can be used along with combination of distributions of snp markers. it is assumed that most of the markers (95%) have minor effects and a few numbers of markers (5%) exert major effects. the simulated population included a basic population of ...

A two-factor experiment with interaction between factors wherein observations follow an Inverse Gaussian model is considered. Analysis of the experiment is approached via an empirical Bayes procedure. The conjugate family of prior distributions is considered. Bayes and empirical Bayes estimators are derived. Application of the procedure is illustrated on a data set, which has previously been an...

Progress in science often comes from discovering invariances in the relationship between variables. Invariances correspond to null hypotheses. As is commonly known, it is not possible to accumulate evidence for the null hypothesis in conventional significance testing. We highlight a Bayes factor alternative to the t-test for accumulating evidence for either the null or alternative hypotheses. T...

Abstract This chapter addresses the problem of discrimination between competing propositions regarding selected features a population interest, also commonly known as “hypothesis testing”. Examples include counting processes when refer to proportion items in given that show forensic interest (e.g., with illegal content). Another typical example is concentration controlled substance, such drugs ...

We have developed a new model selection criteria, the partial intrinsic Bayes factor, which is designed for cases when we select a model among a small number of candidate models. For example, we can choose only a few candidate models after exploring scatter plots. Based on this motivation, the partial intrinsic Bayes factor is developed. By simulation study, we showed that PIBF performs better ...

A computing method of fractional Bayes factor (FBF) for a point null hypothesis is explained. We propose alternative form of FBF that is the product of density ratio and a quantity using the generalized Savage-Dickey density ratio method. When it is diicult to compute the alternative form of FBF analytically, each term of the proposed form can be estimated by MCMC method. Finally, two examples ...

We propose the use of the generalized fractional Bayes factor for testing fit in multinomial models. This is a non-asymptotic method that can be used to quantify the evidence for or against a sub-model. We give expressions for the generalized fractional Bayes factor and we study its properties. In particular, we show that the generalized fractional Bayes factor has better properties than the fr...

the empirical bayes estimators of treatment effects in a factorial experiment were derived and their asymptotic properties were explored. it was shown that they were asymptotically optimal and the estimator of the scale parameter had a limiting gamma distribution while the estimators of the factor effects had a limiting multivariate normal distribution. a bootstrap analysis was performed to ill...

Good large sample performance is typically a minimum requirement of any model selection criterion. This article focuses on the consistency property of the Bayes factor, a commonly used model comparison tool, which has experienced a recent surge of attention in the literature. We thoroughly review existing results. As there exists such a wide variety of settings to be considered, e.g. parametric...