Estimation of general parameter in adaptive cluster sampling using two auxiliary variables

Younis, Faryal; Shabbir, Javid

doi:10.4038/jnsfsr.v47i1.8933

Cited by 2 publications

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“…ACS is relatively new and its theory is being developed [22,23,24,25,26] therefore the use of multi-auxiliary variables and different functions like log and exponential under different transformations have not been extensively studied to develop efficient estimators. Thus in this article we have combined all these ideas and proposed wider classes of estimators based on single and dual auxiliary variables for estimating the finite population mean of the survey variable so that the properties like bias and mean squared errors of various member estimators using several functions like log and exponential under different transformations would be known in advance.…”

Section: Introductionmentioning

confidence: 99%

Wider Classes of Estimators in Adaptive Cluster Sampling

Singh,

Mishra

2023

AJPAS

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Aims/ Objectives: Various efficient estimators using single and dual auxiliary variables with different functions including log and exponential have been developed in the SRSWOR design. Since the Adaptive cluster sampling (ACS) design is relatively new, estimators using functions like log and exponential with single and dual auxiliary variables have not been explored much. Therefore in this article, we propose two wider classes of estimators using single and dual auxiliary variables respectively so that the properties like bias and mean squared errors of various estimators using functions like log and exponential or any other function which belong to the proposed wider classes and have not been developed and studied yet would be known in advance. Formulae of the bias and mean squared error have been derived and presented. Further, since log type estimators have not been studied extensively in the ACS design we have developed new log type classes from each of the proposed wider classes and developed and studied some new log type member estimators. To examine the performance of these new developed log-type estimators over some competing estimators simulation studies have been conducted and all the estimators are further applied to a real data to estimate the average number of Mules in the Indian state of Assam. The studies show that the developed log-type estimators perform better.

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Section: Introductionmentioning

confidence: 99%

Wider Classes of Estimators in Adaptive Cluster Sampling

Singh,

Mishra

2023

AJPAS

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“…Then adaptively increasing the sample using information of neighboring units which satisfy the pre-specified condition. Recent developments in the area of ACS include Chutiman et al [2], Gattone et al [3], Yasmeen and Thompson [4], Qureshi et al [5], Bak [6], Younis and Shabbir [7]- [10].…”

Section: Introductionmentioning

confidence: 99%

Estimation of General Parameter Under Stratified Adaptive Cluster Sampling Based on Dual Use of Auxiliary Information

Younis

Shabbir

2019

Scientia Iranica

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Auxiliary information is mostly used together with study variable to enhance efficiency of estimators for population mean, total and variance. Thompson introduced adaptive cluster sampling as an appropriate sampling scheme for rare and clustered populations. In present article, difference-type and difference-cum-exponential-ratio-type estimators are presented utilizing two auxiliary variables for estimation of general parameter under stratified adaptive cluster sampling. Proposed estimators utilize auxiliary information in terms of ranks, variances and means of auxiliary variables in h th stratum. Expressions for bias and mean square error of proposed estimators are derived using first order of approximation. Numerical study is conducted to evaluate the performance of proposed estimators.

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Estimation of general parameter in adaptive cluster sampling using two auxiliary variables

Cited by 2 publications

References 1 publication

Wider Classes of Estimators in Adaptive Cluster Sampling

Wider Classes of Estimators in Adaptive Cluster Sampling

Estimation of General Parameter Under Stratified Adaptive Cluster Sampling Based on Dual Use of Auxiliary Information

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