2019年
Covariance matrix estimation in a seemingly unrelated regression model under Stein’s loss
Statistical Methods and Applications
- ,
- 巻
- 29
- 号
- 1
- 開始ページ
- 79
- 終了ページ
- 99
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1007/s10260-019-00473-x
- 出版者・発行元
- Springer
© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. A seemingly unrelated regression model has been commonly used for describing a set of different regression models with correlations. This paper discusses the estimation of the covariance matrix in a seemingly unrelated regression model under Stein’s loss function. In particular, when the correlation matrix is assumed to be known, a best equivariant estimator of the covariance matrix is derived. Its properties are investigated and a connection to a best equivariant estimator of regression coefficients given in a previous study is shown. Results of numerical simulations and an illustrative example are also presented to compare the best equivariant estimator of the covariance matrix with several conventional covariance matrix estimators.
- リンク情報
- ID情報
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- DOI : 10.1007/s10260-019-00473-x
- ISSN : 1618-2510
- eISSN : 1613-981X
- SCOPUS ID : 85066491253