We derive a class of matching priors for the shape parameter of the exponential power distribution, which controls the thickness of the density tails. It is shown that a second-order matching prior does not exist in the subclass of the considered priors.

In this paper, the reference prior is developed for a truncated model with boundaries of support as two functions of an unknown parameter. It generalizes the result obtained in a recent paper by Berger et al. (2009), in which a rigorous definition of reference priors was proposed and the prior...

In this paper, objective Bayesian analysis of Pareto distributions under progressive Type-II censoring is considered. Three types of noninformative priors (Jeffreys prior, two reference priors and two general forms of second order probability matching prior) are given. We show that one of the...

With sparse structures and conditional independence, one could estimate the precision matrix of Gaussian graphical models more efficiently. Sun and Sun (2005) studied objective priors for star-shape graphical models. We consider a generative star-shape model. Objective priors such as invariance...

We propose Bayesian model selection based on composite datasets, which can be constructed from various subsample estimates. The method remains consistent without fully specifying a probability model, and is useful for dependent data, when asymptotic variance of the parameter estimator is...

A Bayesian model selection procedure for comparing models subject to inequality and/or equality constraints is proposed. An encompassing prior approach is used, and a general form of the Bayes factor of a constrained model against the encompassing model is derived. A simple estimation method is...

The em algorithm can be used to compute maximum likelihood estimates of model parameters for skew-t mixture models. We show that the intractable expectations needed in the e-step can be written out analytically. These closed form expressions bypass the need for numerical estimation procedures,...

Fisher’s linear discriminant function might be difficult to estimate, when data come from a semiparametric, finite mixture model. We propose an estimator based on the singular value decomposition of the third standardized cumulant. The estimator is consistent when sampling from a mixture of...