Adaptive two-stage sequential double sampling

TL;DR

This paper introduces a multi-phase two-stage sequential double sampling method that leverages auxiliary variables to improve estimation accuracy in surveys where population auxiliary data is limited.

Contribution

It proposes a novel multi-phase variant of two-stage sequential double sampling utilizing auxiliary variables in both design and estimation stages.

Findings

Simulation results demonstrate improved estimation precision.

The method effectively uses auxiliary information without prior knowledge of its population mean.

Performance is validated through simulations based on Median and Thompson (2004).

Abstract

In many surveys inexpensive auxiliary variables are available that can help us to make more precise estimation about the main variable. Using auxiliary variable has been extended by regression estimators for rare and cluster populations. In conventional regression estimator it is assumed that the mean of auxiliary variable in the population is known. In many surveys we don't have such wide information about auxiliary variable. In this paper we present a multi-phase variant of two-stage sequential sampling based on an inexpensive auxiliary variable associated with the survey variable in the form of double sampling. The auxiliary variable will be used in both design and estimation stage. The population mean is estimated by a modified regression-type estimator with two different coefficient. Results will be investigated using some simulations following Median and Thompson (2004).