Analysis of Generalized Exponential Distribution  Under Adaptive Type-II Progressive Hybrid  Censored Competing Risks Data

Ashour, Samir K.; Nassar, Mazen

doi:10.14419/ijasp.v2i2.3423

Cited by 19 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ng et al [6] presented the maximum likelihood estimation (MLE) and asymptotic confidence interval of the failure rate for the exponential distribution and Lin et al [7] discussed the MLE and approximate MLE of the Weibull shape and scale parameters in Type-II APHCS. Mahmoud et al [8] gave the MLE and Bayes estimates considering the generalized Pareto distribution and Ashour and Nassar [9] showed the MLE and asymptotic confidence intervals in the presence of competing risks under Type-II APHCS. Moreover, Ismail [10] studied the MLE of Weibull parameters by tampered random variable model in step-stress partially accelerated life tests based on the adaptive Type-II progressively censoring data.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the extended Weibull parameters and acceleration factors in the step-stress accelerated life tests under an adaptive progressively hybrid censoring data

Zhang

Shi

2016

Journal of Statistical Computation and Simulation

View full text Add to dashboard Cite

Based on the tampered failure rate model under the adaptive Type-II progressively hybrid censoring data, we discuss the maximum likelihood estimators of the unknown parameters and acceleration factors in the general step-stress accelerated life tests in this paper. We also construct the exact and unique confidence interval for the extended Weibull shape parameter. In the numerical analysis, we describe the simulation procedures to obtain the adaptive Type-II progressively hybrid censoring data in the stepstress accelerated life tests and present an experimental data to illustrate the performance of the estimators. ARTICLE HISTORY

show abstract

Section: Introductionmentioning

confidence: 99%

Estimation of the extended Weibull parameters and acceleration factors in the step-stress accelerated life tests under an adaptive progressively hybrid censoring data

Zhang

Shi

2016

Journal of Statistical Computation and Simulation

View full text Add to dashboard Cite

show abstract

“…For the distribution parameters, they calculated the expected Fisher information matrices and MLEs. Generalized exponential distribution with adaptive Type-II progressive hybrid censored competing risks data was studied by Ashour and Nassar [30]. A competing risks model with a generalized Type I hybrid censoring method was presented by Mao et al [31].…”

mentioning

confidence: 99%

Statistical Inference for Competing Risks Model with Adaptive Progressively Type-II Censored Gompertz Life Data Using Industrial and Medical Applications

2022

View full text Add to dashboard Cite

This study uses the adaptive Type-II progressively censored competing risks model to estimate the unknown parameters and the survival function of the Gompertz distribution. Where the lifetime for each failure is considered independent, and each follows a unique Gompertz distribution with different shape parameters. First, the Newton-Raphson method is used to derive the maximum likelihood estimators (MLEs), and the existence and uniqueness of the estimators are also demonstrated. We used the stochastic expectation maximization (SEM) method to construct MLEs for unknown parameters, which simplified and facilitated computation. Based on the asymptotic normality of the MLEs and SEM methods, we create the corresponding confidence intervals for unknown parameters, and the delta approach is utilized to obtain the interval estimation of the reliability function. Additionally, using two bootstrap techniques, the approximative interval estimators for all unknowns are created. Furthermore, we computed the Bayes estimates of unknown parameters as well as the survival function using the Markov chain Monte Carlo (MCMC) method in the presence of square error and LINEX loss functions. Finally, we look into two real data sets and create a simulation study to evaluate the efficacy of the established approaches.

show abstract

Statistical Inference of Burr-XII Distribution Under Adaptive Type II Progressive Censored Schemes with Competing Risks

Gui

2022

Results Math

View full text Add to dashboard Cite

Analysis of Generalized Exponential Distribution Under Adaptive Type-II Progressive Hybrid Censored Competing Risks Data

Cited by 19 publications

References 16 publications

Estimation of the extended Weibull parameters and acceleration factors in the step-stress accelerated life tests under an adaptive progressively hybrid censoring data

Estimation of the extended Weibull parameters and acceleration factors in the step-stress accelerated life tests under an adaptive progressively hybrid censoring data

Statistical Inference for Competing Risks Model with Adaptive Progressively Type-II Censored Gompertz Life Data Using Industrial and Medical Applications

Statistical Inference of Burr-XII Distribution Under Adaptive Type II Progressive Censored Schemes with Competing Risks

Contact Info

Product

Resources

About