Construction of shrinkage estimators for the regression coefficient matrix in the GMANOVA model

Takeaki Kariya, Yoshihiko Konno, William Strawderman

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper extensively investigates the theory of estimating the regression coefficient matrix in the normal GMANOVA model. We explicitly construct estimators which improve upon the maximum likelihood estimator under an invariant scalar loss function. These include the double shrinkage estimators and those shrinking the maximum likelihood estimators directly. The underlying method is the decomposition of the problem into the conditional subproblems due to Kariya, Konno, and Strawderman(1996) and application of integration-by-parts technique to derive an unbiased estimate of the risk for certain class of invariant estimators.

Original languageEnglish (US)
Pages (from-to)597-611
Number of pages15
JournalCommunications in Statistics - Theory and Methods
Volume28
Issue number3-4
DOIs
StatePublished - Jan 1 1999

Fingerprint

Shrinkage Estimator
Regression Coefficient
Maximum Likelihood Estimator
Integration by parts
Estimator
Invariant
Shrinking
Loss Function
Scalar
Decompose
Model
Estimate
Class

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

Keywords

  • Double shrinkage estimators
  • GMANOVA model
  • Integration-by-parts technique
  • Stein effect
  • Unbiased estimate of risk

Cite this

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Construction of shrinkage estimators for the regression coefficient matrix in the GMANOVA model. / Kariya, Takeaki; Konno, Yoshihiko; Strawderman, William.

In: Communications in Statistics - Theory and Methods, Vol. 28, No. 3-4, 01.01.1999, p. 597-611.

Research output: Contribution to journalArticle

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