In this article, Stein-Haff identity is established for a singular Wishart distribution with a positive definite mean matrix but with the dimension larger than the degrees of freedom. This identity is then used to obtain estimators of the precision matrix improving on the estimator based on the...

In this paper, we consider the prediction problem in multiple linear regression model in which the number of predictor variables, p, is extremely large compared to the number of available observations, n. The least squares predictor based on a generalized inverse is not efficient. It is shown...

We propose an information criterion which measures the prediction risk of the predictive density based on the Bayesian marginal likelihood from a frequentist point of view. We derive the criteria for selecting variables in linear regression models by putting the prior on the regression...

<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>

The paper addresses the problem of selecting variables in the two-stage sampling models characterized as a linear mixed model. We obtain the Empirical Bayes Information Criterion (EBIC) using a prior distribution on regression coefficients with an unknown hyper-parameter. It is shown that EBIC...

In this paper, we consider the problem of selecting the variables of the fixed effects in the linear mixed models where the random effects are present and the observation vectors have been obtained from many clusters. As the variable selection procedure, we here use the Akaike Information...

The Akaike Information Criterion (AIC) is developed for selecting the variables of a nested error regression model where an unobservable random effect is present. Using the idea of decomposing the marginal distribution into two parts of 'within' and 'between' analysis of variance, we derive the...

In this paper, we consider the problem of selecting variables from the fixed effects as well as from the random effects when observations from several clusters are available to provide consistent estimators of some unknown parameters. We obtain Bayesian Information Criterion (BIC) using the...

The Akaike information criterion, AIC, and Mallows' Cp statistic have been proposed for selecting a smaller number of regressor variables in the multivariate regression models with fully unknown covariance matrix. All these criteria are, however, based on the implicit assumption that the sample...

The Akaike information criterion (AIC) has been used very successfully in the literature in model selection for small number of parameters pand large number of observations N. The cases when pis large and close to N or when pN have not been considered in the literature. In fact, when pis large...