Browsing by Author "Basant Kumar Ray"

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Generalized Calibration Estimator of Population Mean for Stratified Sampling in the Presence of Non-response
(Springer, 2025) Anant Patel; Basant Kumar Ray; Neha Garg
The present research focuses on developing a comprehensive mathematical framework for estimating the population mean by applying the calibration approach when some units don’t respond (non-response). This study focuses mainly on stratified sampling. The generalized form of the calibration estimators was obtained by minimizing a chi-square type distance function while simultaneously meeting a set of m conditions. The purpose of formulating this general form is to facilitate the deduction of the mean squared error of several previously proposed calibration estimators of the mean for well-stratified groups involving non-response. The general form of the calibration estimators not only allows for obtaining previously proposed calibration-based estimators but also opens the door for developing numerous improved calibration estimators for population mean in the non-response scenarios. The calibration estimator proposed by Dykes et al. (Commun Stat-Theory Methods 44:3403-3427, 2015) and four new calibration estimators were also derived using the suggested general form to support our mathematical framework. The purpose of formulating this general form is to facilitate the deduction of the mean squared error of several previously proposed calibration estimators of the mean for well-stratified groups involving non-response. The simulation study has also been performed in R software generating five different data sets. It exhibited that the proposed calibration estimators perform better than the existing estimators given by Hansen and Hurwitz (1946) as well as Dykes et al. (Commun Stat-Theory Methods 44:3403-3427, 2015) in the presence of non-response. © The Indian Society for Probability and Statistics (ISPS) 2025.
Treating the problem of non-response in stratified random sampling under calibration approach
(Taylor and Francis Ltd., 2025) Manoj Kumar Chaudhary; Basant Kumar Ray
In the present study, we suggested a chi-square-type distance function that works well for non-response situations and we have derived a new calibration-based estimator using such a kind of distance function, which is used to estimate the mean of a stratified population. The proposed estimator is derived through the implementation of a reweighting technique referred to as the calibration approach, aimed at improving upon Hansen and Hurwitz’s (1946) estimator. Typically, in situations where some units do not respond, researchers adjust the stratum weights to obtain an estimator for the mean of a stratified population using the calibration approach. In this study, the weights given to the responding units in the sample and the units in the sub-sample taken from the non-responding sample were calibrated to derive the calibration estimator. We assume that the unit non-response problem impacts both study and auxiliary variables. To examine the performance of the proposed calibration estimator in scenarios involving non-response, we have carried out empirical studies. © 2024 Taylor & Francis Group, LLC.