Covariance matrix estimation in a seemingly unrelated regression model under Stein’s loss

Shun Matsuura, Hiroshi Kurata

研究成果: Article査読

1 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)79-99
ページ数21
ジャーナルStatistical Methods and Applications
29
1
DOI
出版ステータスPublished - 2020 3 1

ASJC Scopus subject areas

  • 統計学および確率
  • 統計学、確率および不確実性

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