Construction of an Almost Unbiased Estimator for a Parameter β using Auxiliary Information
under Simple Random Sampling
Rajesh Singh, Sunil Kumar Yadav
Abstract
This study proposes an almost unbiased estimator for estimation of unknown population regression coefficient (β) in finite population under simple random sampling without replacement (SRSWOR). For this purpose, we have used the estimator proposed by Srivastava et al. (1986), along with two additional estimators recommended by Sukhminder and Sarjinder (1988) for estimation of β. To validate the theoretical results, we used two data sets for empirical study and conducted one simulation study. The simulation study supports the theoretical findings and provides further empirical justification. The estimator’s performance has been assessed using percent relative efficiency (PRE) and mean squared error (MSE) criteria. The results indicate that the proposed class of estimator is more efficient and almost unbiased up to the first order of approximation.
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