Parameter Estimation in Case of Incomplete Frames Using Double Sampling

Sampling frames play an important role in survey research as they lay the foundation on the basis of whichrepresentative sample drawn from a target population. However, in practice, sampling frames are often incomplete, meaningthat they do not fully capture the complete population of interest. In such cases, as a consequence of the incompleteness insampling frame, the sample drawn does not provide a good representation of the population and hence, the results infer tovague and misleading conclusions. The present study is an attempt to somehow uplift the efficacy of the estimator ofpopulation mean by taking into consideration, the additional information gathered from the units currently excluded in theexisting sampling frame along with mean square error of the proposed estimator. Optimum sample sizes are also obtainedusing suitable cost function under Neyman scheme. To compare the proposed estimator with the traditional ones, a simulationstudy has been done