An important aspect of mixture modeling concerns the selection of the number of mixture components. In this paper, we discuss the Bayes factor as a selection tool. The discussion will focus on two aspects: (i) computation of the Bayes factor and (ii) prior sensitivity. For the computation, we propose a variant of Chib’s estimator that accounts for the non-identifiability of the mixture componen...

Discussion Papers are a series of manuscripts in their draft form. They are not intended for circulation or distribution except as indicated by the author. For that reason Discussion Papers may not be reproduced or distributed without the written consent of the author. This paper studies minimaxity of estimators of a set of linear combinations of location parameters µ i , i = 1,. .. , k under q...

The use of a linear estimator to estimate random eeects in a Mixed Model is not necessarily optimal if the prior distribution is non-normal. Either a frequentist or Bayesian approach leads to the Best Linear Unbiased Predictor (BLUP), but an empirical Bayes approach produces a multivariate, non-linear, single-pass kernel-based estimator (the General Empirical Bayes or GEB es-timator) that allow...

A nonparametric mixture model approach to empirical Bayes compound decisions for the Gaussian location model is compared with a parametric empirical Bayes approach recently suggested by Martin and Walker and several recent more formal Bayes procedures. Martin and Walker (2013) have recently proposed a parametric empirical Bayes procedure for the classical Gaussian compound decision problem in w...

This paper provides the estimation of the scale parameter of the exponential distribution under multiply type-II censoring. Using generalized non-informative prior and natural conjugate prior, Bayes estimator and approximate Bayes estimators of the scale parameter have been obtained under square error loss function. The proposed Bayes estimators and approximate Bayes estimators are compared wit...

In this paper we propose a generalised iterative algorithm for calculating variational Bayesian estimates for a normal mixture model and we investigate its convergence properties. It is shown theoretically that the variational Bayes estimator converges locally to the maximum likelihood estimator at the rate of O(1/n) in the large sample limit. We also demonstrate by numerical experiments that t...

This paper devoted a Bayesian and non-Bayesian estimation of the stress-strength reliability, , when X and Y two independent Type I generalized logistic distribution with common scale parameter. The maximum likelihood estimator and Bayes estimator are proposed for the stress strength reliability based on lower record values. The Bayesian and non-Bayesian confidence intervals for the reliability...

This paper considers the estimation of parameters in a dynamic stochastic model for securities prices, where the expected rate of return is a random variable. An empirical Bayes estimator is developed from the model structure. The estimator is an improvement on other popular estimators in terms of mean squared error. The e ect of reduced estimation error on accumulated wealth is analyzed for th...