1 citations · 1 across the 3 of their papers we have counts for
8 papers
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 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…