The Box-Cox transformation is applied to linear mixed models for analyzing positive and clustered data. The problem is that the maximum likelihood estimator of the transformation parameter is not consistent. To fix it, we suggest a simple and consistent estimator for the transformation parameter...

The problem of estimating large covariance matrices with use of factor models is addressed when both the sample size and the dimension of covariance matrix tend to innity. In this paper, we consider a general class of weighted estimators which includes (i) linear combinations of the sample...

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...

The problem of estimating the large covariance matrix of both normal and non-normal distributions is addressed. In convex combinations of the sample covariance　matrix and the identity matrix multiplied by a scalor statistic, we suggest a new　estimator of the optimal weight based on...

　　 This paper addresses the problem of estimating the mean vector of a singular multivariate normal distribution with an unknown singular covariance matrix. The maximum likelihood estimator is shown to be minimax relative to a quadratic loss weighted by the Moore-Penrose inverse of...

　　 For analyzing positive or bounded data, this paper suggests parametrically transformed nested error regression models (TNERM), which not only include the log-transformed model, but also adjust flexibly the transformation parameter to fit the data to a normal linear regression....

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...

The problem of estimating a covariance matrix in multivariate linear regression models is addressed in a decision-theoretic framework. Although a standard loss function is the Stein loss, it is not available in the case of a high dimension. In this paper, a new type of a quadratic loss function,...

　　 This paper addresses the problem of estimating the normal mean matrix with an unknown covariance matrix. Motivated by an empirical Bayes method, we suggest a unied form of the Efron-Morris type estimators based on the Moore-Penrose inverse. This form not only can be dened for...