On best linear and Bayesian linear predictor in calibration

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 (Formula presented.) The outcomes have shown that the error probabilities depend upon (Formula presented.) and to some extent on (Formula presented.) the same invariants upon which the mean squared error of the estimator depends.