Zobrazeno 1 - 10
of 364
pro vyhledávání: '"Kubokawa, Tatsuya"'
Autor:
Kono, Haruki, Kubokawa, Tatsuya
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 delta distribution. Each of them can be interpreted as a fusion of the Ak
Externí odkaz:
http://arxiv.org/abs/2208.09157
Autor:
Yuasa, Ryota, Kubokawa, Tatsuya
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 minimaxity are derived relative to both matrix and scalar quadratic l
Externí odkaz:
http://arxiv.org/abs/2108.06041
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 estimators such as the restricted maximum likelihood estimator. We derive
Externí odkaz:
http://arxiv.org/abs/2105.07563
Autor:
Hamura, Yasuyuki, Kubokawa, Tatsuya
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 variable under the Kullback-Leibler divergence. We assume that these variabl
Externí odkaz:
http://arxiv.org/abs/2006.07052
Autor:
Hamura, Yasuyuki, Kubokawa, Tatsuya
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 of probabilities on the basis of observations of negative multinomial
Externí odkaz:
http://arxiv.org/abs/2001.09602
Autor:
Yuasa, Ryota, Kubokawa, Tatsuya
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 estimators for the precision matrix instead of the Moore-Penrose gene
Externí odkaz:
http://arxiv.org/abs/1910.11984
Autor:
Yuasa, Ryota, Kubokawa, Tatsuya
Publikováno v:
In Journal of Multivariate Analysis March 2023 194
Autor:
Yuasa, Ryota, Kubokawa, Tatsuya
Publikováno v:
In Journal of Statistical Planning and Inference January 2023 222:182-194
Autor:
Ito, Tsubasa, Kubokawa, Tatsuya
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 estimated by a Prasad-Rao type consistent estimator, and the empirical
Externí odkaz:
http://arxiv.org/abs/1804.09941
Autor:
Ito, Tsubasa, Kubokawa, Tatsuya
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 unbiased predictor (EBLUP) of a small area characteristic based on second-ord
Externí odkaz:
http://arxiv.org/abs/1804.09940