Maximum Likelihood Estimation of the Flexible Weibull Distribution Based on Type II censored Data

2011 ◽  
Vol 8 (1) ◽  
pp. 1-23
Author(s):  
Mahmoud Reyad
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Censored Data ◽  
Likelihood Estimation ◽  
Type Ii ◽  
Type Ii Censored Data

Random Censored Data MLE of Weibull Distribution Based on Simulink

2012 ◽  
Vol 542-543 ◽  
pp. 1463-1466
Author(s):  
Jing Zhao Shi ◽  
Rui Na Shao ◽  
Xiang Fu Li
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Censored Data ◽  
Computer Programming ◽  
Likelihood Estimation ◽  
Random Censoring ◽  
Life Data ◽  
Transcendental Equations

Maximum likelihood estimation (MLE) is a commonly used method of Weibull distribution, but it needs to calculate the transcendental equations which is based on the computer programming. In this paper, we established a new MLE model of Weibull distribution in a case of random censoring. And this model was proved feasible and correct via three examples. At the same time, the model can be used for a variety of censored data and complete life data of Weibull distribution of MLE.

Estimation of P(X

2017 ◽  
Vol 34 (7) ◽  
pp. 1111-1122 ◽  
Author(s):  
Soumya Roy ◽  
Biswabrata Pradhan ◽  
E.V. Gijo
Keyword(s):  
Maximum Likelihood ◽  
Censored Data ◽  
Quality Characteristic ◽  
Logistic Distribution ◽  
Type Ii ◽  
Finite Sample ◽  
Data Set ◽  
Content Type ◽  
Type Ii Censored Data ◽  
Interval Estimators

Purpose The purpose of this paper is to compare various methods of estimation of P(X<Y) based on Type-II censored data, where X and Y represent a quality characteristic of interest for two groups. Design/methodology/approach This paper assumes that both X and Y are independently distributed generalized half logistic random variables. The maximum likelihood estimator and the uniformly minimum variance unbiased estimator of R are obtained based on Type-II censored data. An exact 95 percent maximum likelihood estimate-based confidence interval for R is also provided. Next, various Bayesian point and interval estimators are obtained using both the subjective and non-informative priors. A real life data set is analyzed for illustration. Findings The performance of various point and interval estimators is judged through a detailed simulation study. The finite sample properties of the estimators are found to be satisfactory. It is observed that the posterior mean marginally outperform other estimators with respect to the mean squared error even under the non-informative prior. Originality/value The proposed methodology can be used for comparing two groups with respect to a suitable quality characteristic of interest. It can also be applied for estimation of the stress-strength reliability, which is of particular interest to the reliability engineers.

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