16 citations · 53 across the 27 of their papers we have counts for
27 papers
Consistent Bayesian Information Criterion Based on a Mixture Prior for Possibly High-Dimensional Multivariate Linear Regression Models
Haruki Kono, Tatsuya Kubokawa
In the problem of selecting variables in a multivariate linear regression model, we derive new Bayesian information criteria based on a prior mixing a smooth distribution and a del…
Generalized Bayes Estimators with Closed forms for the Normal Mean and Covariance Matrices
Ryota Yuasa, Tatsuya Kubokawa
In the estimation of the mean matrix in a multivariate normal distribution, the generalized Bayes estimators with closed forms are provided, and the sufficient conditions for their…
General Unbiased Estimating Equations for Variance Components in Linear Mixed Models
Tatsuya Kubokawa, Shonosuke Sugasawa, Hiromasa Tamae +1
This paper introduces a general framework for estimating variance components in the linear mixed models via general unbiased estimating equations, which include some well-used esti…
Bayesian Predictive Density Estimation for a Chi-squared Model Using Information from a Normal Observation with Unknown Mean and Variance
Yasuyuki Hamura, Tatsuya Kubokawa
In this paper, we consider the problem of estimating the density function of a Chi-squared variable on the basis of observations of another Chi-squared variable and a normal variab…
Bayesian Shrinkage Estimation of Negative Multinomial Parameter Vectors
Yasuyuki Hamura, Tatsuya Kubokawa
The negative multinomial distribution is a multivariate generalization of the negative binomial distribution. In this paper, we consider the problem of estimating an unknown matrix…
Ridge-type Linear Shrinkage Estimation of the Matrix Mean of High-dimensional Normal Distribution
Ryota Yuasa, Tatsuya Kubokawa
The estimation of the mean matrix of the multivariate normal distribution is addressed in the high dimensional setting. Efron-Morris-type linear shrinkage estimators based on ridge…
Corrected Empirical Bayes Confidence Region in a Multivariate Fay-Herriot Model
Tsubasa Ito, Tatsuya Kubokawa
In the small area estimation, the empirical best linear unbiased predictor (EBLUP) in the linear mixed model is useful because it gives a stable estimate for a mean of a smallarea.…
On Measuring the Variability of Small Area Estimators in a Multivariate Fay-Herriot Model
Tsubasa Ito, Tatsuya Kubokawa
This paper is concerned with the small area estimation in the multivariate Fay-Herriot model where covariance matrix of random effects are fully unknown. The covariance matrix is e…
Empirical Best Linear Unbiased Predictors in Multivariate Nested-Error Regression Models
Tsubasa Ito, Tatsuya Kubokawa
For analyzing unit-level multivariate data in small area estimation, we consider the multivariate nested error regression model (MNER) and provide the empirical best linear unbiase…
Bayes Minimax Competitors of Preliminary Test Estimators in k Sample Problems
Ryo Imai, Tatsuya Kubokawa, Malay Ghosh
In this paper, we consider the estimation of a mean vector of a multivariate normal population where the mean vector is suspected to be nearly equal to mean vectors of other…
Bayesian Simultaneous Estimation for Means in Sample Problems
Ryo Imai, Tatsuya Kubokawa, Malay Ghosh
This paper is concerned with the simultaneous estimation of population means when one suspects that the means are nearly equal. As an alternative to the preliminary test es…
Adaptively Transformed Mixed Model Prediction of General Finite Population Parameters
Shonosuke Sugasawa, Tatsuya Kubokawa
For estimating area-specific parameters (quantities) in a finite population, a mixed model prediction approach is attractive. However, this approach strongly depends on the normali…
Proper Bayes and Minimax Predictive Densities for a Matrix-variate Normal Distribution
Hisayuki Tsukuma, Tatsuya Kubokawa
This paper deals with the problem of estimating predictive densities of a matrix-variate normal distribution with known covariance matrix. Our main aim is to establish some Bayesia…
Empirical Uncertain Bayes Methods in Area-level Models
Shonosuke Sugasawa, Tatsuya Kubokawa, Kota Ogasawara
Random effects model can account for the lack of fitting a regression model and increase precision of estimating area-level means. However, in case that the synthetic mean provides…
Bayesian Estimators in Uncertain Nested Error Regression Models
Shonosuke Sugasawa, Tatsuya Kubokawa
Nested error regression models are useful tools for analysis of grouped data, especially in the case of small area estimation. This paper suggests a nested error regression model u…
Transforming response values in small area prediction
Shonosuke Sugasawa, Tatsuya Kubokawa
In real applications of small area estimation, one often encounters data with positive response values. The use of a parametric transformation for positive response values in the F…
Small Area Predictors with Dual Shrinkage of Means and Variances
Hiromasa Tamae, Tatsuya Kubokawa
The paper concerns small-area estimation in the Fay-Herriot type area-level model with random dispersions, which models the case that the sampling errors change from area to area.…
Bayesian Estimators for Small Area Models Shrinking Both Means and Variances
Shonosuke Sugasawa, Hiromasa Tamae, Tatsuya Kubokawa
For small area estimation of area-level data, the Fay-Herriot model is extensively used as a model based method. In the Fay-Herriot model, it is conventionally assumed that the sam…
Heteroscedastic Nested Error Regression Models with Variance Functions
Shonosuke Sugasawa, Tatsuya Kubokawa
The nested error regression model is a useful tool for analyzing clustered (grouped) data, and is especially used in small area estimation. The classical nested error regression mo…
A Variant of AIC based on the Bayesian Marginal Likelihood
Yuki Kawakubo, Tatsuya Kubokawa, Muni S. Srivastava
We propose information criteria that measure the prediction risk of a predictive density based on the Bayesian marginal likelihood from a frequentist point of view. We derive crite…
Consistent Estimation in Box-Cox Transformed Linear Mixed Models
Shonosuke Sugasawa, Tatsuya Kubokawa
The Box-Cox transformation is applied to the linear mixed models for analyzing positive and grouped data. The problem in using Box Cox transformation is that the maximum likelihood…
Conditional Akaike information under covariate shift with application to small area estimation
Yuki Kawakubo, Shonosuke Sugasawa, Tatsuya Kubokawa
In this study, we consider the problem of selecting explanatory variables of fixed effects in linear mixed models under covariate shift, which is when the values of covariates in t…
On Conditional Prediction Errors in Mixed Models with Application to Small Area Estimation
Shonosuke Sugasawa, Tatsuya Kubokawa
The empirical Bayes estimators in mixed models are useful for small area estimation in the sense of increasing precision of prediction for small area means, and one wants to know t…
Parametric Transformed Fay-Herriot Model for Small Area Estimation
Shonosuke Sugasawa, Tatsuya Kubokawa
In this paper, we consider parametric transformed Fay-Herriot models, and clarify conditions on transformations under which the estimator of the transformation is consistent. It is…