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Some New Regression Estimators for Incomplete Sampling Frames

Shilpa Yadav, Pankaj Nagar

Abstract



A crucial element in any research or survey process, the sampling frame serves as the foundation upon which the entire structure of data collection is built. However, inherent imperfections can render these frameworks incomplete, potentially leading to a skewed representation of the target population. In the realm of statistics and social research, the concept of incomplete sampling frames poses a pertinent challenge, impacting the accuracy and reliability of findings. As such, comprehending the ramifications of incomplete sampling frames within the context of research methodologies is pivotal in discerning the extent of its influence on data quality and the subsequent implications for decision-making and policy formulation. The present study tackles with the problem of incompleteness in sampling frames using information on the unincluded units as well as some auxiliary information through regression estimators. Combined and separate regression estimators are developed using the concept of stratification in double sampling paradigm and their relative efficiencies are compared. Further, a simulation study is conducted to numerically illustrate the efficacy of the proposed estimators.

Keywords


incomplete sampling frame, double sampling, regression method of estimation, predecessorsuccessor method, auxiliary variable, mean square error

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