A unified approach to estimation of noncentrality parameters, the multiple correlation coefficient, and mixture models

T. Kubokawa, Marchand, W. E. Strawderman

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We consider a class of mixture models for positive continuous data and the estimation of an underlying parameter θ of the mixing distribution. With a unified approach, we obtain classes of dominating estimators under squared error loss of an unbiased estimator, which include smooth estimators. Applications include estimating noncentrality parameters of chi-square and F-distributions, as well as ρ2/(1 − ρ2), where ρ is amultivariate correlation coefficient in a multivariate normal set-up. Finally, the findings are extended to situations, where there exists a lower bound constraint on θ.

Original languageEnglish (US)
Pages (from-to)134-148
Number of pages15
JournalMathematical Methods of Statistics
Volume26
Issue number2
DOIs
StatePublished - Apr 1 2017

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • estimation
  • lower bounded parameter
  • mixtures
  • multiple correlation coefficient
  • noncentrality parameter

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