主  題: An Introduction to Bayesian Hypothesis Testing for ANOVA Designs 

內(nèi)容簡(jiǎn)介: We explore Bayesian approaches for the hypothesis testing problem in multiway ANOVA models. We first specialize the result in a two-sample scenario as an intermediate step toward developing the Bayes factors for ANOVA designs. Given that the design matrix is not necessarily of full rank, we adopt the sum-to-zero constraint for uniqueness and employ the singular value decomposition (SVD) method to reparameterize the model to get rid of the additional constraint. We then derive the Bayes factors under a class of Zellner’s g-priors. We examine asymptotic properties of the proposed procedures with a diverging dimensionality. Our results indicate that commonly used hyper-priors yield inconsistent Bayes factors due to the presence of an inconsistency region around the null model. We propose a new class of hyper-priors to avoid this inconsistency problem. Simulation studies on two-way ANOVA models are conducted to compare the performance of the proposed priors with that of some existing ones in the literature.

報(bào)告人: 汪敏    副教授    博士

時(shí)  間: 2018-07-08    10:00 

地  點(diǎn): 競(jìng)慧東樓302 

舉辦單位: 統(tǒng)計(jì)與數(shù)學(xué)學(xué)院