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Point Estimation and Confidence Set in a Parallelism Model: an Empirical Bayes Approach

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  • Christian P. Robert
  • A. K. Md. Ehsanes Saleh

Abstract

When several simple regression models are assumed to have similar slopes, empirical Bayes methods can efficiently process tis vague information by estimating the hyperparameters of a conjugate prior. The shrinkage estimators we obtain are shown to be minimax and, furthermore, dominate usual confidence regions in terms of coverage probability.

Suggested Citation

  • Christian P. Robert & A. K. Md. Ehsanes Saleh, 1991. "Point Estimation and Confidence Set in a Parallelism Model: an Empirical Bayes Approach," Annals of Economics and Statistics, GENES, issue 23, pages 65-89.
    • Handle: RePEc:adr:anecst:y:1991:i:23:p:65-89
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    File URL: http://www.jstor.org/stable/20075835
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    Cited by:

    1. Mohammad Arashi & Mahdi Roozbeh, 2015. "Shrinkage estimation in system regression model," Computational Statistics, Springer, vol. 30(2), pages 359-376, June.

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