A Hybrid Exponential-Generalized Gamma Distribution with Mean Bases Mixing Proportion: Theory and Applications
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Date
2026-04-01
Journal Title
Journal ISSN
Volume Title
Publisher
FUDMA JOURNAL OF SCIENCES
Abstract
In our study, we propose the Exponential-Generalized Gamma Distribution (EGGD) with Mean-Based Mixing
Proportion. A new hybrid survival distribution developed to overcome the limitations of existing parametric
models in modeling complex hazard functions and structures. The EGGD combines the simplicity of the
exponential distribution with the flexibility of the generalized gamma distribution. The analytical calculations
of the distribution’s important statistical properties, namely moments, skewness, kurtosis, survival, and hazard
functions, have been derived to provide further insights into the distribution’s behavior. The EGGD parameter
estimation is conducted using maximum likelihood estimation (MLE). The performance of the maximum
likelihood estimates was rigorously examined through a Monte Carlo simulation study. The performance
measures used in the study were bias and MSE. The practicality of the Model was examined through its
application to real-world lifetime data. Its performance was compared with that of other existing three
parameter and two-parameter lifetime distributions. The model adequacy is assessed using information criteria,
including AIC, AICc, HQIC, and BIC. Across three datasets, the EGGD consistently exhibits superior
goodness-of-fit compared to the other considered models, highlighting its flexibility and robustness as a tool
for survival and reliability analysis.