This paper is concerned with estimation of a predictive density with parametric constraints under Kullback-Leibler loss. When an invariance structure is embed- ded in the problem, general and unied conditions for the minimaxity of the best equivariant predictive density estimator are derived....

We consider minimax shrinkage estimation of a location vector of a spherically symmetric distribution under a loss function which is a concave function of the usual squared error loss. In particular for distributions which are scale mixtures of normals (and somewhat more generally), and for...

Our investigation concerns the estimation of predictive densities and a study of effiency as measured by the frequentist risk of such predictive densities with integrated L2 and L1 losses. Our findings relate to a p-variate spherically symmetric observable X ∼ px (||x -μ||2) and the...

This paper studies decision theoretic properties of benchmarked estimators which are of some importance in small area estimation problems. Benchmarking is intended to improve certain aggregate properties (such as study-wide averages) when model based estimates have been applied to individual...

This paper studies minimaxity of estimators of a set of linear combinations of location parameters μi, i = 1, . . . , k under quadratic loss. When each location parameter is known to be positive, previous results about minimaxity or non-minimaxity are extended from the case of estimating a...

The estimation of a linear combination of several restricted location parameters is addressed from a decision-theoretic point of view. The corresponding linear combination of the best location equivariant and the unrestricted unbiased estimators is minimax. Since the locations are restricted, it...

<p>In this article, we propose tests for covariance matrices of high dimension with fewer observations than the dimension for a general class of distributions with positive definite covariance matrices. In one-sample case, tests are proposed for sphericity and for testing the hypothesis that the...</p>

In this paper, we suggest the new variable selection procedure, called MEC, for linear discriminant rule in the high-dimensional setup. MEC is derived as a second-order unbiased estimator of the misclassication error probability of the lin- ear discriminant rule. It is shown that MEC not only...

The paper concerns small-area estimation in the heteroscedastic nested error regression (HNER) model which assumes that the within-area variances are different among areas. Although HNER is useful for analyzing data where the within-area variation changes from area to area, it is difficult to...

This paper is concerned with the prediction of the conditional mean which involves the fixed and random effects based on the natural exponential family with a quadratic variance function. The best predictor is interpreted as the Bayes estimator in the Bayesian context, and the empirical Bayes...