This study introduces a parsimonious and tractable generator for continuous distribution called the Teissier-G family of distributions for continuous random variables and examines the distributions belonging to this family as the sub-models. Some general statistical characteristics and sub-models of the new generator were examined and studied. Similarly, we examined the shapes of the sub-models probability density function (pdf) and hazard rate function were investigated. The parameter of the proposed model was obtained in a closed form by maximum likelihood. In addition to the numerical real life applications, Monte Carlo simulation was performed to examine the flexibility of the introduced models. The models provide good fits in all the cases. The results show great improvement compared to existing models.

This paper introduces a two-parameters generator of continuous statistical probability distributions called the Alpha Power Rayleigh-G (APRAY-G) family, some statistical properties of the family of distributions were derived, and we introduced a two-submodels of the generator. We estimate the parameters of the models based on the method of maximum likelihood estimation and explored simulation studies based on the introduced submodels. We observed that the biasedness and root mean square errors decrease as the sample size becomes large. We examined the applications of the models based on real-life data sets. We compared the obtained results with some existing probability distribution models. The results showed that the proposed models gave a better fitness to the data under investigation.

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This article introduced the determination of reliability analysis of the alpha power Gompertz model using the Bayesian techniques. The method developed has been evaluated using women breast cancer in the Stan implementation in R. A survival data used illustrates the proposed Bayesian approach.