The Doubly Generalized Weibull Power Series Frailty Distribution

This paper introduces the doubly generalized Weibull power series frailty (DGWPSF) model, an extension of the Weibull–k-truncated power series family incorporating a gamma-frailty term. The model enhances flexibility for lifetime data by accommodating unobserved heterogeneity and latent risk factors in survival and reliability studies. We derive its fundamental properties, including the probability density, distribution, survival, and hazard functions, and highlight notable special cases, including the binomial, Poisson, geometric, and logarithmic models. To address the challenges of parameter estimation, we develop the expectation-maximization algorithm and the Bayesian inference procedures. The DGWPSF framework offers a flexible structure for lifetime data analysis, capturing diverse frailty patterns while improving model interpretability and robustness.

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Publication Details

Journal
International Journal of Analysis and Applications
Published
2025-10-16
DOI
https://doi.org/10.28924/2291-8639-23-2025-255
Citations
1
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
2.63

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article

The Doubly Generalized Weibull Power Series Frailty Distribution

Mohieddine Rahmouni
1 citations
International Journal of Analysis and Applications
Statistical Distribution Estimation and Applications
2.63
article

The Doubly Generalized Weibull Power Series Frailty Distribution

Mohieddine Rahmouni
article en
1 citations

Abstract

This paper introduces the doubly generalized Weibull power series frailty (DGWPSF) model, an extension of the Weibull–k-truncated power series family incorporating a gamma-frailty term. The model enhances flexibility for lifetime data by accommodating unobserved heterogeneity and latent risk factors in survival and reliability studies. We derive its fundamental properties, including the probability density, distribution, survival, and hazard functions, and highlight notable special cases, including the binomial, Poisson, geometric, and logarithmic models. To address the challenges of parameter estimation, we develop the expectation-maximization algorithm and the Bayesian inference procedures. The DGWPSF framework offers a flexible structure for lifetime data analysis, capturing diverse frailty patterns while improving model interpretability and robustness.

International Journal of Analysis and ApplicationsVol. 23
King Faisal University (SA)
King Faisal University
Openalex Percentile: Top 8%
Statistical Distribution Estimation and Applications
2.63
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The Doubly Generalized Weibull Power Series Frailty Distribution — Mohieddine Rahmouni · International Journal of Analysis and Applications (2025) | TGRS Research Map | TGRS