Generalized Exponential-Cum-Exponential Estimator in Adaptive Cluster Sampling

Chaudhry, Muhammad Shahzad; Hanif, Muhammad

doi:10.18187/pjsor.v11i4.1009

Cited by 4 publications

(6 citation statements)

References 14 publications

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“…. The values of ( ) u v, can be found in Table [6] and Table [7]. For more comprehensive information, refer to Yasmeen and Thompson (2020).…”

Section: Simulated Poisson Cluster Process Data (Population-3)mentioning

confidence: 99%

“…For more comprehensive information, refer to Yasmeen and Thompson (2020). The results of the numerical study are provided in Tables [8]. which represents the Mean Squared Error (MSE).…”

Section: Simulated Poisson Cluster Process Data (Population-3)mentioning

confidence: 99%

“…which represents the Mean Squared Error (MSE). In Table [8], the MSE clearly indicates the minimum MSE of the proposed estimators compared to the adapted estimators.…”

Section: Simulated Poisson Cluster Process Data (Population-3)mentioning

confidence: 99%

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Generalized Robust Regression techniques and adaptive cluster sampling for efficient estimation of population mean in case of Rare and Clustered Populations

Subzar,

Wani,

Raja

et al. 2024

Preprint

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In many situations, when field researchers are inclined to deviate from the preselected sample plan and then to include nearby or related units in the sample, adaptive cluster sampling provides a nearby complete solution. For both rare and clustered populations, Thompson has introduced the adaptive cluster sampling (ACS) as a suitable sampling method when the data is not contaminated with outliers. So keeping this thing in mind, the present study focusses on defining adaptive ratio type regression estimators using OLS, Huber M, Mallows GM, Schweppe GM and SIS GM estimation functions within the framework of ACS. Subsequently, we have proposed regression type estimators utilizing OLS, Huber M, Mallows GM, Schweppe GM and SIS GM estimation functions within the framework of ACS. In this study we have also derived the mean square error property of both the adapted and proposed estimators. To evaluate the performance of these estimators we have used both real life and simulated Poisson clustered process data sets.

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“…. The values of ( ) u v, can be found in Table [6] and Table [7]. For more comprehensive information, refer to Yasmeen and Thompson (2020).…”

Section: Simulated Poisson Cluster Process Data (Population-3)mentioning

confidence: 99%

“…For more comprehensive information, refer to Yasmeen and Thompson (2020). The results of the numerical study are provided in Tables [8]. which represents the Mean Squared Error (MSE).…”

Section: Simulated Poisson Cluster Process Data (Population-3)mentioning

confidence: 99%

See 1 more Smart Citation

Generalized Robust Regression techniques and adaptive cluster sampling for efficient estimation of population mean in case of Rare and Clustered Populations

Subzar,

Wani,

Raja

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Since ACS is relatively new, estimators using multi-auxiliary information are not much explored and thus in this sub-section, we present a related estimator based on two auxiliary variables proposed by Chaudhry and Hanif [21] as follows:…”

Section: Related Estimators Using Dual Auxiliary Variablesmentioning

confidence: 99%

“…Qureshi et al [20] proposed their estimator for the population mean of the survey variable using some robust measures. Chaudhry and Hanif [21] proposed an estimator using two auxiliary variables for estimating the population mean of the survey variable.…”

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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Ratio-cum-product Type Estimators for Rare and Hidden Clustered Population

Singh

Mishra

2022

Sankhya B

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The use of multi-auxiliary variables helps in increasing the precision of the estimators, especially when the population is rare and hidden clustered. In this article, four ratio-cum-product type estimators have been proposed using two auxiliary variables under adaptive cluster sampling (ACS) design. The expressions of the mean square error (MSE) of the proposed ratio-cum-product type estimators have been derived up to the first order of approximation and presented along with their efficiency conditions with respect to the estimators presented in this article. The efficiency of the proposed estimators over similar existing estimators have been assessed on four different populations two of which are of the daily spread of COVID-19 cases. The proposed estimators performed better than the estimators presented in this article on all four populations indicating their wide applicability and precision.

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Generalized Exponential-Cum-Exponential Estimator in Adaptive Cluster Sampling

Cited by 4 publications

References 14 publications

Generalized Robust Regression techniques and adaptive cluster sampling for efficient estimation of population mean in case of Rare and Clustered Populations

Generalized Robust Regression techniques and adaptive cluster sampling for efficient estimation of population mean in case of Rare and Clustered Populations

Wider Classes of Estimators in Adaptive Cluster Sampling

Ratio-cum-product Type Estimators for Rare and Hidden Clustered Population

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