On maximum likelihood prediction based on Type II doubly censored exponential data

Metrika ◽  
2000 ◽  
Vol 50 (3) ◽  
pp. 211-220 ◽  
Author(s):  
Arturo J. Fernández
Keyword(s):  
Maximum Likelihood ◽  
Type Ii ◽  
Doubly Censored ◽  
Maximum Likelihood Prediction

Computing maximum likelihood estimates from type II doubly censored exponential data

2002 ◽  
Vol 11 (2) ◽  
pp. 187-200 ◽  
Author(s):  
Arturo J. fernández ◽  
José I. Bravo ◽  
Íñigo De Fuentes
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimates ◽  
Type Ii ◽  
Doubly Censored

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.

scholarly journals Reliability Inference for the Multicomponent System Based on Progressively Type II Censored Samples from Generalized Pareto Distributions

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1176
Author(s):  
Lauren Sauer ◽  
Yuhlong Lio ◽  
Tzong-Ru Tsai
Keyword(s):  
Maximum Likelihood ◽  
Confidence Interval ◽  
System Reliability ◽  
Bootstrap Method ◽  
Multicomponent System ◽  
Type Ii ◽  
Censored Samples ◽  
Generalized Pareto ◽  
Pareto Distributions ◽  
Generalized Pareto Distributions

In this paper, the reliability of a k-component system, in which all components are subject to common stress, is considered. The multicomponent system will continue to survive if at least s out of k components’ strength exceed the common stress. The system reliability is investigated by utilizing the maximum likelihood estimator based on progressively type II censored samples from generalized Pareto distributions. The confidence interval of the system reliability can be obtained by using asymptotic normality with Fisher information matrix or bootstrap method approximation. An intensive simulation study is conducted to evaluate the performance of maximum likelihood estimators of the model parameters and system reliability for a variety of cases. For the confidence interval of the system reliability, simulation results indicate the bootstrap method approximation outperforms over the asymptotic normality approximation in terms of coverage probability.

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