Formulation of estimator for population mean in stratified successive sampling using memory-based information

Sanjoy  Majumder; Arnab Bandyopadhyay; Arindam Gupta

doi:https://doi.org/10.59139/stattrans-2025-015

Formulation of estimator for population mean in stratified successive sampling using memory-based information

Sanjoy Majumder Department of Mathematics and Statistics, Aliah University, Kolkata-700160, India ORCID:https://orcid.org/0000-0001-8320-003x , Arnab Bandyopadhyay Department of Basic Science and Humanities (Mathematics), Dr. B. C. Roy Engineering College, Durgapur 713206, India ORCID:https://orcid.org/0000-0002-0769-7491 , Arindam Gupta Department of Statistics, The University of Burdwan, Burdwan-713104, India ORCID:https://orcid.org/0000-0001-9381-5150 Statistics in Transition new series, vol. 26, 2025, 2, pages: 39-56 Published online: 13 June 2025 https://doi.org/10.59139/stattrans-2025-015 Citation: Majumder S., Bandyopadhyay A., Gupta A., 2025. Formulation of estimator for population mean in stratified successive sampling using memory-based Information. Statistics in Transition new series, 26(2), pp. 39-56; https://doi.org/10.59139/stattrans-2025-015

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ABSTRACT

In study described in this article, we developed a memory type estimator for the population mean in stratified successive sampling. We used the past sample information together with the current sample information through hybrid exponentially weighted moving averages statistics. We have also used the information available on auxiliary variable to construct the proposed estimator. We studied the properties of the proposed estimator. Further, we examined the performance of the proposed estimator in comparison with conventional estimator of the population mean and the results are demonstrated by using the data set of simulation as well as natural population. After observing the auspicious findings, we suggest that the proposed estimator can be applied to solve real-life problems.

KEYWORDS

successive sampling, HEWMA, regression estimator, variance, minimum variance, efficiency.

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