 ### Journals Information

Mathematics and Statistics Vol. 10(3), pp. 659 - 669
DOI: 10.13189/ms.2022.100323
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## Statistical Inference of Modified Kies Exponential Distribution Using Censored Data

1 Department of Mathematics, College of Science , Jouf University, Saudi Arabia
2 Department of Mathematics, Faculty of Science, Minia University, Egypt

ABSTRACT

This paper deals with obtaining the interval and point estimation to Modified Kies exponential distribution in case of progressive first failure (PFF) censored data. It uses two approaches, classical and non-classical methods of estimation, including the highest posterior density (HPD). We obtained the Maximum Likelihood Estimation of the parameters and the logarithm likelihood function, and we used the maximum likelihood estimation of the parameters as a classical approach. We calculated the confidence intervals for the parameters and the Bootstrap confidence Intervals. We employed the posterior distribution and the Bayesian estimation (BE) under different loss functions (Symmetric loss function, The MCMC usage, and The M-H algorithm). Some results depending on simulation data are adopted to explain estimation methods. We used various censoring schemes and various sample sizes to determine whether the sample size affects the estimation measures. We used different confidence intervals to determine the best and shortest intervals. Also, the major findings in the paper are remarked on in the conclusion section.

KEYWORDS
Modified Kies Exponential Distribution, Bayesian Estimation, Progressive First Failure, Maximum Likelihood Estimation

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
 Fathy H. Riad , "Statistical Inference of Modified Kies Exponential Distribution Using Censored Data," Mathematics and Statistics, Vol. 10, No. 3, pp. 659 - 669, 2022. DOI: 10.13189/ms.2022.100323.

(b). APA Format:
Fathy H. Riad (2022). Statistical Inference of Modified Kies Exponential Distribution Using Censored Data. Mathematics and Statistics, 10(3), 659 - 669. DOI: 10.13189/ms.2022.100323.