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On best linear and Bayesian linear predictor in calibration

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  • Faqir Muhammad
  • Muhammad Riaz
  • Hassan Dawood
  • Hussain Dawood

Abstract

The availability of some prior information, along with the current, may help us to improve the properties of statistical techniques. In this study, Bayesian best linear predictor is derived for simple and multivariate calibration situations. A comparative study of the mean squared errors of the Bayesian best linear predictor and the best linear predictor (classical) shows that Bayesian best linear predictor performs equally well. Interval estimates, both for known and unknown parameters, of the best linear predictor have been considered using different pivotal functions and different distributions for p(t). The outcomes have shown that the error probabilities depend upon N,BN,CN and to some extent on ρ, the same invariants upon which the mean squared error of the estimator depends.

Suggested Citation

  • Faqir Muhammad & Muhammad Riaz & Hassan Dawood & Hussain Dawood, 2022. "On best linear and Bayesian linear predictor in calibration," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(11), pages 3669-3693, June.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:11:p:3669-3693
    DOI: 10.1080/03610926.2020.1801733
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