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Product and Exponential Product Estimators in Adaptive Cluster Sampling under Different Population Situations


Article Information

Title: Product and Exponential Product Estimators in Adaptive Cluster Sampling under Different Population Situations

Authors: Muhammad Shahzad Chaudhry, Muhammad Hanif

Journal: Proceedings of the Pakistan Academy of Sciences: A

HEC Recognition History
Category From To
Y 2024-10-01 2025-12-31
Y 2023-07-01 2024-09-30
Y 2022-07-01 2023-06-30
Y 2021-07-01 2022-06-30
Y 2020-07-01 2021-06-30

Publisher: "Physical And Computational Sciences. Pakistan Academy of Sciences."

Country: Pakistan

Year: 2016

Volume: 53

Issue: 4

Language: English

Keywords: Transformed PopulationAuxiliary informationsimulated populationbivariate normal distributionnegative correlationexpected final sample sizecomparable varianceestimated relative bias

Categories

Abstract

In this paper, the product and exponential product estimators have been proposed for estimating the population mean using population mean of an auxiliary variable, when there is negative correlation between the variables, under adaptive cluster sampling (ACS) design. The expressions for mean squared error (MSE) and bias of the proposed estimators have been derived. Two simulated populations are used and simulation studies have been conceded out to reveal and match the efficiencies of the estimators. The proposed estimators have been matched with conventional estimators and estimators in ACS. The simulation results showed that the proposed product and exponential product estimators are more efficient as compare to conventional as well as Hansen-Hurwitz and ratio estimators in ACS.


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