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EFFICIENCY-ENHANCED POPULATION MEAN ESTIMATION UNDER STRATIFIED SAMPLING WITH INDETERMINATE DATA

Muhammad Waqar Hussain et al.

Advances and Applications in Statistics2026https://doi.org/10.17654/0972361726019article
ABDC C
Weight
0.50

What the paper says

Neutrosophic estimation is a major development in sampling theory that successfully tackles the problems posed by uncertain, indeterminate and unreliable data. This study estimates the population mean of the study variable by incorporating auxiliary information within a neutrosophic environment under stratified sampling. A neutrosophic stratified exponential ratio-type estimator is aimed at improving estimation accuracy in heterogeneous neutrosophic populations. The proposed estimator enhances both precision and reliability. It assesses the bias and mean square error (MSE) through first-order approximations. The theoretical findings are supported by empirical evidence, which demonstrates the estimator’s superior performance, achieving lower MSEs and higher percent relative efficiencies (PREs) when compared to conventional estimators. These results affirm the proposed estimator as a reliable and efficient tool for future applications in neutrosophic stratified sampling (NSS), particularly in environments characterized by indeterminate data.

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https://doi.org/https://doi.org/10.17654/0972361726019

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@article{muhammad2026,
  title        = {{EFFICIENCY-ENHANCED POPULATION MEAN ESTIMATION UNDER STRATIFIED SAMPLING WITH INDETERMINATE DATA}},
  author       = {Muhammad Waqar Hussain et al.},
  journal      = {Advances and Applications in Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.17654/0972361726019},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.