Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical

Hamdy, Ahlam; Almetwally, Ehab M.

doi:10.21608/cjmss.2023.185497.1001

Cited by 5 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The parameters of the CBHE distribution are estimated in this section using maximum likelihood, maximum product spacing, ordinary least squares, weighted least squares, Cramér-von Mises, percentile and Anderson-Darling estimation methods. Recently papers have been discussed the estimation methods for parameter of distribution modeling as [13,14,15].…”

Section: Parameter Estimationmentioning

confidence: 99%

Chen Burr-Hatke Exponential Distribution: Properties, Regressions and Biomedical Applications

Abubakari¹,

Anzagra

Nasiru³

2023

Computational Journal of Mathematical and Statistical Sciences

View full text Add to dashboard Cite

Accurately modeling lifetime data is very important for appropriate decision making in health and biomedical fields. This usually requires the use of distributions. However, no single distribution can model all types of data. Hence, the development of distributions with appropriate usefulness is very important for modeling purposes. In this study, a new lifetime distribution, known as Chen Burr-Hatke exponential distribution is proposed. The objective of the study is to obtain a new lifetime distribution which can serve as an alternative distribution to modeling lifetime data. Also, such a distribution can be used to provide inferences via regression models. Plots of the density function of the new distribution show that the distribution can exhibit increasing, decreasing, right-skewed and left-skewed shapes. Also, plots of the hazard rate function show that the distribution can exhibit increasing, decreasing, and upside down bathtub shapes. Statistical properties, such as the quantile function, moments, order statistics and inequality measures, are derived. Several estimation methods are used to estimate the parameters of the distribution. Using Monte Carlo simulations, the estimators were all consistent. However, maximum likelihood estimation method was observed to better estimate the parameters of the distribution. Two regression models based on the distribution are established. The usefulness of the distribution and its regression models are demonstrated using real lifetime datasets. The results show that the models can provide a good fit to lifetime data, and hence can serve as alternative models to fitting such data.

show abstract

Section: Parameter Estimationmentioning

confidence: 99%

Chen Burr-Hatke Exponential Distribution: Properties, Regressions and Biomedical Applications

Abubakari¹,

Anzagra

Nasiru³

2023

Computational Journal of Mathematical and Statistical Sciences

View full text Add to dashboard Cite

show abstract

“…Gamma priors' mean and variance can be used to represent the derived hyper-parameters. For more information see [35][36][37]. The parameters γ l , β l , and λ l where l = 1, 2, of BFGMPLx distribution should be wellknown and positive.…”

Section: Bayesian Estimationmentioning

confidence: 99%

Bivariate power Lomax distribution with medical applications

Qura

Fayomi

Kilai³

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

In this paper, a bivariate power Lomax distribution based on Farlie-Gumbel-Morgenstern (FGM) copulas and univariate power Lomax distribution is proposed, which is referred to as BFGMPLx. It is a significant lifetime distribution for modeling bivariate lifetime data. The statistical properties of the proposed distribution, such as conditional distributions, conditional expectations, marginal distributions, moment-generating functions, product moments, positive quadrant dependence property, and Pearson’s correlation, have been studied. The reliability measures, such as the survival function, hazard rate function, mean residual life function, and vitality function, have also been discussed. The parameters of the model can be estimated through maximum likelihood and Bayesian estimation. Additionally, asymptotic confidence intervals and credible intervals of Bayesian’s highest posterior density are computed for the parameter model. Monte Carlo simulation analysis is used to estimate both the maximum likelihood and Bayesian estimators.

show abstract

“…Set the initial value of as Chen and Shao (1999) is used to provide the 95% two-sided greatest density region credible interval for the unknown parameters or any function of them. For more recently papers, see Tolba (2022), Salem et al (2023), and Hamdy et al (2023).…”

Section: Bayesian Estimationmentioning

confidence: 99%

Inference of Inverted Exponentiated Lomax Distribution based on Step-Stress Partially Accelerated Life Test under Progressive Type II Censoring

Assar,

Almetwally

2023

The Egyptian Statistical Journal

View full text Add to dashboard Cite

Due to the importance of the problem of testing the product units under stress higher than normal stress conditions, specially used in reliability analysis. In this paper, we discuss the problem of estimation with step stress partially accelerated life tests, the lifetime of testing items under use condition follows the inverted exponentiated Lomax distribution. The test is running under progressive Type-II censoring scheme, and the units drawn from the test were distributed as a binomial distribution. The model parameters and acceleration factor are estimated by maximum likelihood and Bayesian methods. The corresponding asymptotic confidence intervals as well as credible intervals are also constructed. Also, the theoretical results are assessed and compared through Monte Carlo simulation study. Two real data set are used to illustrate how the approaches will perform in practice. Finally, we reported some comments about numerical computation.

show abstract

Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical

Cited by 5 publications

References 27 publications

Chen Burr-Hatke Exponential Distribution: Properties, Regressions and Biomedical Applications

Chen Burr-Hatke Exponential Distribution: Properties, Regressions and Biomedical Applications

Bivariate power Lomax distribution with medical applications

Inference of Inverted Exponentiated Lomax Distribution based on Step-Stress Partially Accelerated Life Test under Progressive Type II Censoring

Contact Info

Product

Resources

About