On Inference of Weitzman Overlapping Coefficient in Two Weibull Distributions

Studying overlapping coefficients has recently become of great benefit, especially after its use in goodness-of-fit tests. These coefficients are defined as the amount of similarity between two statistical distributions. This research examines the estimation of one of these overlapping coefficients, which is the Weitzman coefficient Δ, assuming two Weibull distributions and without using any restrictions on the parameters of these distributions. We studied the relative bias and relative mean square error of the resulting estimator by implementing a simulation study. The results show the importance of the resulting estimator.

Authors

Publication Details

Journal
Journal of Health Statistics Reports
Published
2024-09-19
DOI
https://doi.org/10.47363/jhsr/2024(3)122
Citations
1
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
0.71
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article

On Inference of Weitzman Overlapping Coefficient in Two Weibull Distributions

Omar Eidous
1 citations
Journal of Health Statistics Reports
Statistical Distribution Estimation and Applications
0.71
article

On Inference of Weitzman Overlapping Coefficient in Two Weibull Distributions

Omar Eidous
article en
1 citations

Abstract

Studying overlapping coefficients has recently become of great benefit, especially after its use in goodness-of-fit tests. These coefficients are defined as the amount of similarity between two statistical distributions. This research examines the estimation of one of these overlapping coefficients, which is the Weitzman coefficient Δ, assuming two Weibull distributions and without using any restrictions on the parameters of these distributions. We studied the relative bias and relative mean square error of the resulting estimator by implementing a simulation study. The results show the importance of the resulting estimator.

Journal of Health Statistics Reports
Openalex Percentile: Top 26%
Statistical Distribution Estimation and Applications
0.71
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