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

Younis, Faryal; Shabbir, Javid

doi:10.24200/sci.2019.52515.2753

Cited by 2 publications

(3 citation statements)

References 26 publications

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“…The bias of this class can be obtained by putting H1 = ∂δ ∂u u=1 (20). Similarly the MSE can be obtained by putting H1 = 1 and H2 = − 1 2 in ( 22)-( 23) and putting it in (24). The exact expressions of bias and MSE are…”

Section: Log Type Class Derived From T W Imentioning

confidence: 99%

“…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%

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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: Log Type Class Derived From T W Imentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wider Classes of Estimators in Adaptive Cluster Sampling

Singh,

Mishra

2023

AJPAS

View full text Add to dashboard Cite

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“…Some other useful references are, Irfan et al [17], Abid et al. [18], Abid et al [19], Javed et al [20], Naz et al [21], Younis and Shabbir [22], Ahmed and Shabbir [23] and Nazir et al [24].…”

Section: Introductionmentioning

confidence: 99%

On use of subsampling of the non-respondents for estimation of distribution function

Shabbir

Gupta

2021

Scientia Iranica

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In this study, we propose a general class of estimators of the finite population distribution function (DF) using two auxiliary variables under subsampling of non-respondents.We use the Hansen and Hurwitz pioneered model in our subsampling technique. Layout of response and non-response classes are discussed in various tables in detail. Expressions for the biases and mean square errors (MSEs) of the estimators are obtained up to first order of approximation. We also obtain the conditions by comparing the proposed estimator with existing estimators. Three real data sets are used to support the theoretical findings. In our findings, it is observed that the proposed class of estimators is more efficient as compared to all other existing estimators including the usual mean estimator, ratio estimator, exponential-ratio estimator, traditional difference estimator, and many well-known difference type estimators by using the criterion of MSE.

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

Cited by 2 publications

References 26 publications

Wider Classes of Estimators in Adaptive Cluster Sampling

Wider Classes of Estimators in Adaptive Cluster Sampling

On use of subsampling of the non-respondents for estimation of distribution function

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