Generalized Class of Finite Population Variance in the Presence of Random Nonresponse Using Simulation Approach

Javed, Sapna; Masood, Saadia; Shokri, Ali

doi:10.1155/2023/6643435

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…Further, ref. [29] introduced finite population variance estimation in random responses via SRS for applied and environmental sciences and proved its effectiveness over others. Refs.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Variance Estimation in Stratified Random Sampling through a Log-Type Estimator for Finite Populations

Triveni,

Danish,

Albalawi

2024

Symmetry

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In this research, a logarithmic-type estimator was formulated for estimating the finite population variance in stratified random sampling. By ensuring that the sampling process is symmetrically conducted across the population, biases can be minimized, and the sample is more likely to be representative of the population as a whole. We conducted a comprehensive numerical study and simulation study to evaluate the performance of the proposed estimator. The mean squared error values were computed for both our proposed estimator and several existing ones, including the standard unbiased variance estimator, difference-type estimator, and other considered estimators. The results of the numerical study and simulation study demonstrated that the proposed log-type estimator outperforms the other considered estimators in terms of MSE and percentage relative efficiency. Graphical representations of the results are also provided to illustrate the efficiency of the proposed estimator. Based on the findings of this study, we conclude that the proposed log-type estimator is a valuable addition to the existing literature on variance estimation in stratified random sampling. It provides a more efficient and accurate estimate of the population variance, which can be beneficial for various statistical applications.

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“…Further, ref. [29] introduced finite population variance estimation in random responses via SRS for applied and environmental sciences and proved its effectiveness over others. Refs.…”

Section: Introductionmentioning

confidence: 99%