Hybrid class of robust type estimators for variance estimation using mean and variance of auxiliary variable

Alomair, Mohammed Ahmed; Hussain Shah Gardazi, Syed Aflake

doi:10.1016/j.heliyon.2024.e31039

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…For estimating the finite population variance, a number of researchers have proposed different kinds of estimators, including [8][9][10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Double Exponential Ratio Estimator of a Finite Population Variance under Extreme Values in Simple Random Sampling

Daraz,

Wu,

Albalawi

2024

Mathematics

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This article presents an improved class of efficient estimators aimed at estimating the finite population variance of the study variable. These estimators are especially useful when we have information about the minimum/maximum values of the auxiliary variable within a framework of simple random sampling. The characteristics of the proposed class of estimators, including bias and mean squared error (MSE) under simple random sampling are derived through a first-order approximation. To assess the performance and validate the theoretical outcomes, we conduct a simulation study. Results indicate that the proposed class of estimators has lower MSEs as compared to other existing estimators across all simulation scenarios. Three datasets are used in the application section to emphasize the effectiveness of the proposed class of estimators over conventional unbiased variance estimators, ratio and regression estimators, and other existing estimators.

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“…For estimating the finite population variance, a number of researchers have proposed different kinds of estimators, including [8][9][10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Double Exponential Ratio Estimator of a Finite Population Variance under Extreme Values in Simple Random Sampling

Daraz,

Wu,

Albalawi

2024

Mathematics

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“…Using stratified sampling, homogeneous groups known as strata are generated from different groups, and samples can be taken from each stratum independently. A lot of researchers have worked on different types of estimators to estimate the finite population variance, including [1] , [5] , [6] , [14] , [15] , [16] , [21] , [22] , [23] , [24] , [27] , [28] , [29] .…”

Section: Introductionmentioning

confidence: 99%

New classes of difference cum-ratio-type exponential estimators for a finite population variance in stratified random sampling

Daraz,

Wu,

Alomair

et al. 2024

Heliyon

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Efficient Class of Variance Estimators for Population using Supplementary Information in Stratified Random Sampling

Hussain,

Khan,

Zaman

et al. 2024

VFAST trans. math.

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This paper addresses an efficient class of variance estimators for population using stratified random sampling. The suggested class of estimators using supplementary information has been studied in different circumstances. The expressions of bias and mean square error (MSE) of the proposed estimators are derived up to the first degree of approximation. The theoretical comparison of the proposed and considered estimators is also discussed, which shows that the proposed estimators are more efficient than the existing estimators. Theoretical findings are validated by three different types of real data sets and simulation studies. The numerical results of the proposed and existing estimators are compared in terms of mean square error, percentage relative efficiency and diagrams. It is observed that all the proposed estimators outperform the existing estimators. For instance, the traditional unbiased estimator Ozel et.al [6] and other existing estimators. Lastly, appropriate recommendations have been provided for researchers to use these suggested estimators to solve real-world issues.

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Hybrid class of robust type estimators for variance estimation using mean and variance of auxiliary variable

Cited by 3 publications

References 11 publications

Double Exponential Ratio Estimator of a Finite Population Variance under Extreme Values in Simple Random Sampling

Double Exponential Ratio Estimator of a Finite Population Variance under Extreme Values in Simple Random Sampling

New classes of difference cum-ratio-type exponential estimators for a finite population variance in stratified random sampling

Efficient Class of Variance Estimators for Population using Supplementary Information in Stratified Random Sampling

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