Iterative weighted least-squares estimates in a heteroscedastic linear regression model

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Abstract

The aim of this study is to improve the e0ciency of weighted least-squares estimates for a regression parameter. An iterative procedure, starting with an unbiased estimate other than the unweighted least-squares estimate, yields estimates which are asymptotically more e0cient than the feasible generalized least-squares estimate when errors are spherically distributed. The result has an application in the improvement of the Graybill-Deal estimate of the common mean of several normal populations.

Original languageEnglish
Pages (from-to)133-146
Number of pages14
JournalJournal of Statistical Planning and Inference
Volume110
Issue number1-2
Publication statusPublished - 2003 Jan 15
Externally publishedYes

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Keywords

  • Asymptotic variance
  • Common mean
  • Graybill-Deal estimate
  • Heteroscedastic linear regression
  • Iterative procedure
  • Replication
  • Spherical distribution

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Applied Mathematics
  • Statistics and Probability

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