Tatsuya Kubokawa

Commun. Stat. Simul. Comput., March, 2024

2023

Weighted shrinkage estimators of normal mean matrices and dominance properties.

[BibT_eX]

[DOI]

Ryota Yuasa

J. Multivar. Anal., 2023

2021

Shrinkage estimation with singular priors and an application to small area estimation.

[BibT_eX]

[DOI]

J. Multivar. Anal., 2021

2020

Ridge-type linear shrinkage estimation of the mean matrix of a high-dimensional normal distribution.

[BibT_eX]

[DOI]

Ryota Yuasa

J. Multivar. Anal., 2020

Bayesian shrinkage estimation of negative multinomial parameter vectors.

[BibT_eX]

[DOI]

Yasuyuki Hamura

J. Multivar. Anal., 2020

2019

Bayesian simultaneous estimation for means in k-sample problems.

[BibT_eX]

[DOI]

Ryo Imai

Malay Ghosh

J. Multivar. Anal., 2019

2017

Proper Bayes and minimax predictive densities related to estimation of a normal mean matrix.

[BibT_eX]

[DOI]

J. Multivar. Anal., 2017

Bayesian estimators in uncertain nested error regression models.

[BibT_eX]

[DOI]

J. Multivar. Anal., 2017

Transforming response values in small area prediction.

[BibT_eX]

[DOI]

Comput. Stat. Data Anal., 2017

2016

Unified improvements in estimation of a normal covariance matrix in high and low dimensions.

[BibT_eX]

[DOI]

J. Multivar. Anal., 2016

On conditional prediction errors in mixed models with application to small area estimation.

[BibT_eX]

[DOI]

J. Multivar. Anal., 2016

Linear shrinkage estimation of large covariance matrices using factor models.

[BibT_eX]

[DOI]

Yuki Ikeda

J. Multivar. Anal., 2016

Comparison of linear shrinkage estimators of a large covariance matrix in normal and non-normal distributions.

[BibT_eX]

[DOI]

Yuki Ikeda

Comput. Stat. Data Anal., 2016

2015

Estimation of the mean vector in a singular multivariate normal distribution.

[BibT_eX]

[DOI]

J. Multivar. Anal., 2015

A unified approach to estimating a normal mean matrix in high and low dimensions.

[BibT_eX]

[DOI]

J. Multivar. Anal., 2015

Parametric transformed Fay-Herriot model for small area estimation.

[BibT_eX]

[DOI]

J. Multivar. Anal., 2015

On predictive density estimation for location families under integrated squared error loss.

[BibT_eX]

[DOI]

Éric Marchand

William E. Strawderman

J. Multivar. Anal., 2015

2014

Tests for covariance matrices in high dimension with less sample size.

[BibT_eX]

[DOI]

Hirokazu Yanagihara

J. Multivar. Anal., 2014

Modified conditional AIC in linear mixed models.

[BibT_eX]

[DOI]

Yuki Kawakubo

J. Multivar. Anal., 2014

A variable selection criterion for linear discriminant rule and its optimality in high dimensional and large sample data.

[BibT_eX]

[DOI]

Masashi Hyodo

J. Multivar. Anal., 2014

2013

Tests for multivariate analysis of variance in high dimension under non-normality.

[BibT_eX]

[DOI]

J. Multivar. Anal., 2013

Minimaxity in predictive density estimation with parametric constraints.

[BibT_eX]

[DOI]

Éric Marchand

William E. Strawderman

Jean-Philippe Turcotte

J. Multivar. Anal., 2013

Asymptotic expansion and estimation of EPMC for linear classification rules in high dimension.

[BibT_eX]

[DOI]

Masashi Hyodo

J. Multivar. Anal., 2013

Constrained empirical Bayes estimator and its uncertainty in normal linear mixed models.

[BibT_eX]

[DOI]

J. Multivar. Anal., 2013

Bartlett-type adjustments for hypothesis testing in linear models with general error covariance matrices.

[BibT_eX]

[DOI]

Masahiro Kojima

J. Multivar. Anal., 2013

2012

Parametric bootstrap methods for bias correction in linear mixed models.

[BibT_eX]

[DOI]

Bui Nagashima

J. Multivar. Anal., 2012

2011

Modifying estimators of ordered positive parameters under the Stein loss.

[BibT_eX]

[DOI]

J. Multivar. Anal., 2011

A unified approach to non-minimaxity of sets of linear combinations of restricted location estimators.

[BibT_eX]

[DOI]

William E. Strawderman

J. Multivar. Anal., 2011

Conditional and unconditional methods for selecting variables in linear mixed models.

[BibT_eX]

[DOI]

J. Multivar. Anal., 2011

2010

Conditional information criteria for selecting variables in linear mixed models.

[BibT_eX]

[DOI]