Best equivariant estimator of regression coefficients in a seemingly unrelated regression model with known correlation matrix

Hiroshi Kurata, Shun Matsuura

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This paper derives the best equivariant estimator (BEE) of the regression coefficients of a seemingly unrelated regression model with an elliptically symmetric error. Equivariance with respect to the group of location and scale transformations is considered. We assume that the correlation matrix of the error term is known. Since the correlation matrix is a maximal invariant parameter under the group action, the model treated in this paper is generated as exactly one orbit on the parameter space. It is also shown that the BEE can be viewed as a generalized least squares estimator.

Original languageEnglish
Pages (from-to)705-723
Number of pages19
JournalAnnals of the Institute of Statistical Mathematics
Volume68
Issue number4
DOIs
Publication statusPublished - 2016 Aug 1

Keywords

  • Equivariant estimator
  • Generalized least squares estimator
  • Group invariance
  • Maximal invariant
  • Seemingly unrelated regression model

ASJC Scopus subject areas

  • Statistics and Probability

Fingerprint Dive into the research topics of 'Best equivariant estimator of regression coefficients in a seemingly unrelated regression model with known correlation matrix'. Together they form a unique fingerprint.

  • Cite this