Empirical Bayes sequential estimation of a finite population mean

ABSTRACT

In this article, we consider the Bayes and empirical Bayes problem of the current population mean of a finite population when the sample data is available from other similar (m-1) finite populations. We investigate a general class of linear estimators and obtain the optimal linear Bayes estimator of the finite population mean under a squared error loss function that considered the cost of sampling. The optimal linear Bayes estimator and the sample size are obtained as a function of the parameters of the prior distribution. The corresponding empirical Bayes estimates are obtained by replacing the unknown hyperparameters with their respective consistent estimates. A Monte Carlo study is conducted to evaluate the performance of the proposed empirical Bayes procedure.

KEYWORDS:

• Empirical Bayes
• sequential
• estimation
• Monte Carlo simulation
• finite population

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This work was supported by Qatar National Research Fund: [Grant Number JSREP 3 - 017 - 1 - 005].