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 which representative sample drawn from a target population. However, in practice, sampling frames are often incomplete, meaning that they do not fully capture the complete population of interest. In such cases, as a consequence of the incompleteness in sampling frame, the sample drawn does not provide a good representation of the population and hence, the results infer to vague and misleading conclusions. The present study is an attempt to somehow uplift the efficacy of the estimator of population mean by taking into consideration, the additional information gathered from the units currently excluded in the existing sampling frame along with mean square error of the proposed estimator. Optimum sample sizes are also obtained using suitable cost function under Neyman scheme. To compare the proposed estimator with the traditional ones, a simulation study has been done.. KEYWORDS :Incomplete sampling frame, Double sampling, Stratified sampling, Predecessor-successor method, Mean square error.