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New Calibrated Estimators of Population Distribution Function Using Non-linear Calibration Constraints

S. A. Saleem Basha & M. Usman

Model Assisted Statistics and Applications2025https://doi.org/10.1177/15741699251355924article
ABDC C
Weight
0.37

What the paper says

This paper introduces a new calibration estimation technique for the estimation of the population distribution function (DF). We have introduced a new class of calibrated estimators using the non-linear constraints of an auxiliary variable in a simple random sampling design. Their performances have been assessed based on some real and artificially generated data sets under numerical and simulation studies. It has been found that the proposed estimators attain lower absolute relative bias (ARB), lower mean squared error (MSE) and higher percentage relative efficiency (PRE) against the usual unbiased, ratio, product, regression and GREG estimators. The results highlight the effectiveness of the proposed estimators, which may further encourage survey practitioners in their real-life applications.

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https://doi.org/https://doi.org/10.1177/15741699251355924

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@article{s.2025,
  title        = {{New Calibrated Estimators of Population Distribution Function Using Non-linear Calibration Constraints}},
  author       = {S. A. Saleem Basha & M. Usman},
  journal      = {Model Assisted Statistics and Applications},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/15741699251355924},
}

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Evidence weight

0.37

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

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
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.