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

Shun Matsuura, Hiroshi Kurata

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
JournalStatistical Methods and Applications
DOIs
Publication statusPublished - 2019 Jan 1

Keywords

  • Correlation matrix
  • Covariance matrix
  • Equivariant estimator
  • Generalized least squares estimator
  • Seemingly unrelated regression model

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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