Evolving techniques for enhanced estimation: A comprehensive survey of stratified sampling and post-stratification methods

Abstract

This comprehensive review delves into the landscape of estimators used in stratified sampling and post-stratification to estimate crucial population parameters, such as mean, median, variance, proportion, and distribution function. This study extensively examines the utilization of single and double auxiliary information, calibration weights, and various classes of estimators in the context of survey sampling, shedding light on their strengths and limitations. While these methods contribute significantly to enhancing estimation accuracy within stratified populations, this Review underscores challenges such as the dependence on accurate auxiliary information for single and double auxiliary methods, the assumption of ignorable nonresponse in calibration weights, and the need for careful consideration of underlying assumptions in a class of estimators. Future work in this field should focus on refining and validating these methodologies, addressing potential biases, and exploring innovative ways to handle nonresponses. Rigorous sensitivity analyses and investigations into the robustness of these techniques under varying conditions will be essential for advancing the precision and reliability of parameter estimation in the complex landscape of stratified sampling and post-stratification. This Review serves as a foundational resource for researchers and practitioners engaged in advancing the field and navigating the nuances of precise parameter estimation in survey sampling.