The asymptotically optimal empirical bayes estimation in multiple linear regression model

Zhang Shunpu, Wei Laisheng

Research output: Contribution to journalArticle

3 Scopus citations


Empirical Bayes estimation of the parameter vector θ=(β',σ2)' in a multiple linear regression model Y=Xβ+ε is considered, where β is the vector of regression coefficient, ε{reversed tilde}N(0,σI with σ2 unknown. In this paper, we construct the EB estimators of θ by using the kernel estimation of multivariate density function and its partial derivatives. Under some moment conditions on prior distribution we obtain their asymptotic optimality.

Original languageEnglish (US)
Pages (from-to)245-258
Number of pages14
JournalApplied Mathematics-A Journal of Chinese Universities
Issue number3
StatePublished - Sep 1 1994



  • 1991 MR Subject Classification: 62C12
  • Empirical Bayes estimation
  • asymptotic optimality
  • multiple linear regression model

ASJC Scopus subject areas

  • Applied Mathematics

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