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Computing the estimator of a parameter vector via a competing Bayes method

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  • Wang, Lichun

Abstract

Bayesian analysis of normally distributed data with unknown mean and unknown variance is complicated. For the normal distribution N(μ,σ2), a linear Bayes procedure is suggested to simultaneously estimate the parameters μ and σ2. Compared with the usual Bayes estimator and the Lindley approximation, the proposed linear Bayes estimator is simple and easy to use, and some numerical examples are presented to verify its accuracies. Also, the superiorities of the linear Bayes estimator over classical estimators are established in terms of mean squared error matrix criterion.

Suggested Citation

  • Wang, Lichun, 2019. "Computing the estimator of a parameter vector via a competing Bayes method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 165(C), pages 271-279.
    • Handle: RePEc:eee:matcom:v:165:y:2019:i:c:p:271-279
    DOI: 10.1016/j.matcom.2019.03.011
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    References listed on IDEAS

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    1. Samaniego, Francisco J. & Vestrup, Eric, 1999. "On improving standard estimators via linear empirical Bayes methods," Statistics & Probability Letters, Elsevier, vol. 44(3), pages 309-318, September.
    2. Chansoo Kim & Jinhyouk Jung & Younshik Chung, 2011. "Bayesian estimation for the exponentiated Weibull model under Type II progressive censoring," Statistical Papers, Springer, vol. 52(1), pages 53-70, February.
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