Byeonghui Kim

This article deals with non-informative priors for parameters when both stress and strength follow generalized gamma distributions. First, the orthogonal reparameterization is treated and then, using this reparameterization, Jeffreys’ prior, group ordering reference priors, and matching priors are derived. The propriety of posterior distributions is investigated and marginal posterior distributions are provided under those non-informative priors. The question of whether or not the reference prio

Consider the problem of estimating under squared error loss an arbitrarily positive, strictly increasing or decreasing parametric function based on a sample of size n in an one parameter nonregular family of absolutly continuous distributions with both endpoints of the support depending on a single parameter. We first provide sufficient conditions for the admissibility of generalized Bayes estimators with respect to some specific priors and then treat several examples which illustrate the admiss

Suppose a prior is specified only on the interest parameter and a posterior distribution, free from nuisance parameters, is considered on the basis of the profile likelihood or an adjusted version thereof. In this setup, we derive higher order asymptotic results on the construction of confidence intervals that have approximately correct posterior as well as frequentist coverage. Apart from meeting both Bayesian and frequentist objectives under prior specification on the interest parameter alone,

ABSTRACT In this paper we first present general solutions to a differential inequality. These results include some of those of Berger, Ghosh and Parsian, and Ghosh, Hwang and Tsui[1] Berger, J. 1980. Improving on Inadmissible Estimators in Continuous Exponential Families with Applications to Simultaneous Estimation of Gamma Scale Parameters. Ann. Statist., 8: 545–571. [Crossref], [Web of Science ®] , [Google Scholar], 6-7 Ghosh, M., Hwang, J. and Tsui, K. 1984. Construction of Improved Estimator

In this paper, the problem of estimating a p-variate (p≥4) normal mean vector is considered in decision-theoretic set up. Using a simple property of the noncentral chi-square distribution, a sequence of estimators dominating the Lindley type estimator with the cases of unknown covariance matrices has been produced and each improved estimator is beter than previous one.

Consider an estimation problem in the one parameter exponential family of distributions under squared error loss. Das Gupta and Sinha (1984) and Meeden and Ghosh gave, using an approach given in Brown and Hwang (1982) which is in turn based on Blyth's (1951) method, two different sets of sufficient conditions for admissibility of generalized Bayes estimators of an arbitrary parametric function. These two sets of sufficient conditions are discussed and compared;Also, using Karlin's technique, suf

Consider an estimation problem under squared error loss in a family of non-regular densities with both terminals of the support being decreasing functions of an unknown parameter. Using Karlin's(1958) technique, sufficient conditions are given for generalized Bayes estimators to be admissible for estimating an arbitrarily positive, monotone parametric function and then treat some examples which illustrate our results.

The paper compares the Bhattacharyya bounds and the Hammersley-Chapman-Robbins bounds for the variance of unbiased estimators in the presence of nuisance parameters.

We consider the problem of estimating the error variance of in a two-way mixed-eects ANOVA model using noninformative priors. First, we derive Jereys’ prior, a

In the problem of estimating the rror variance in the balanced xed-eects one-way analysis of variance (ANOVA) model, Ghosh (1994) pro-posed hierarchical Bayes estimators and raised a conjecture for which all ofhis hierarchical Bayes estimators are admissible. In this paper we prove thisconjecture is true by representing one-way ANOVA model to the distribu-tional form of a multiparameter exponential family.AMS 2000 subject classications.Primary 62C15; Secondary 62F10.Keywords.Error variance, mult

Consider the problem of estimating the parameter of the model in which an observable random variable is represented by a unknown scalar parameter plus another random variable and the parameter, sample, and decision spaces consist of all integers. We first characterize the class of all admissible estimators and then characterize the class of all finitely admissible estimators. Finally, we show that two classes are identical.