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A Novel Extension of the Weibull Distribution with Application in Quantitative and Reliability Sciences
by
Elkalzah, Bassant
, Iqbal, Shoaib
, Jamal, Farrukh
, Hussain, Zawar
in
Bivariate analysis
/ Datasets
/ Empirical analysis
/ Flexibility
/ Graphical representations
/ Kurtosis
/ Maximum likelihood method
/ Parameter estimation
/ Random sampling
/ Reliability
/ Simulation
/ Skewness
/ Statistical analysis
/ Survival analysis
/ Weibull distribution
2026
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A Novel Extension of the Weibull Distribution with Application in Quantitative and Reliability Sciences
by
Elkalzah, Bassant
, Iqbal, Shoaib
, Jamal, Farrukh
, Hussain, Zawar
in
Bivariate analysis
/ Datasets
/ Empirical analysis
/ Flexibility
/ Graphical representations
/ Kurtosis
/ Maximum likelihood method
/ Parameter estimation
/ Random sampling
/ Reliability
/ Simulation
/ Skewness
/ Statistical analysis
/ Survival analysis
/ Weibull distribution
2026
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Do you wish to request the book?
A Novel Extension of the Weibull Distribution with Application in Quantitative and Reliability Sciences
by
Elkalzah, Bassant
, Iqbal, Shoaib
, Jamal, Farrukh
, Hussain, Zawar
in
Bivariate analysis
/ Datasets
/ Empirical analysis
/ Flexibility
/ Graphical representations
/ Kurtosis
/ Maximum likelihood method
/ Parameter estimation
/ Random sampling
/ Reliability
/ Simulation
/ Skewness
/ Statistical analysis
/ Survival analysis
/ Weibull distribution
2026
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A Novel Extension of the Weibull Distribution with Application in Quantitative and Reliability Sciences
Journal Article
A Novel Extension of the Weibull Distribution with Application in Quantitative and Reliability Sciences
2026
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Overview
The main focus of this paper is to introduce a new probability model. Specifically, this paper presents a modified form of the Weibull distribution and investigates its various statistical properties, such as moments, moment-generating functions, reliability functions, quantile functions, and inequality measures such as Bonferroni and Lorenz curves. It also investigates the mean absolute deviation and entropy. Distributions of order statistics, reversed order statistics, and upper record values are also obtained. Additionally, univariate and bivariate moment structures are considered. The model parameters are estimated via the maximum likelihood method under simple random sampling and ranked set sampling, allowing an empirical evaluation of efficiency and reliability. Graphical representations exhibit the flexibility of the model, capturing various shapes in the probability density and hazard rate functions. To measure the practical quality of the model, actuarial metrics are used. A comparative analysis based on insurance, biomedical, and reliability datasets demonstrates the empirically improved performance and stability of the proposed new model for these specific datasets.
Publisher
MDPI AG
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