scholarly journals Inference on Constant-Stress Accelerated Life Testing Based on Geometric Process for Extension of the Exponential Distribution under Type-II Progressive Censoring

2018 ◽  
Vol 14 (2) ◽  
pp. 233 ◽  
Author(s):  
Abd El-Raheem Mohamed ◽  
S. E. Abu-Youssef ◽  
Nahed S. A. Ali ◽  
A. M. Abd El-Raheem
Keyword(s):  
Exponential Distribution ◽  
Accelerated Life Testing ◽  
Constant Stress ◽  
Type Ii ◽  
Life Testing ◽  
Progressive Censoring ◽  
Geometric Process ◽  
Accelerated Life

scholarly journals Bayesian Estimation and Prediction Based on Constant Stress-Partially Accelerated Life Testing for Topp Leone-Inverted Kumaraswamy Distribution

2021 ◽  
pp. 11-36
Author(s):  
G. R. Al-Dayian ◽  
A. A. El-Helbawy ◽  
R. M. Refaey ◽  
S. M. Behairy
Keyword(s):  
Loss Function ◽  
Accelerated Life Testing ◽  
Constant Stress ◽  
Type Ii ◽  
Life Testing ◽  
Bayes Estimators ◽  
Kumaraswamy Distribution ◽  
Accelerated Life Tests ◽  
Accelerated Life ◽  
Life Tests

Accelerated life testing or partially accelerated life tests is very important in life testing experiments because it saves time and cost. Partially accelerated life tests are used when the data obtained from accelerated life tests cannot be extrapolated to usual conditions. This paper proposes, constant–stress partially accelerated life test using Type II censored samples, assuming that the lifetime of items under usual condition have the Topp Leone-inverted Kumaraswamy distribution. The Bayes estimators for the parameters, acceleration factor, reliability and hazard rate function are obtained. Bayes estimators based on informative priors is derived under the balanced square error loss function as a symmetric loss function and balanced linear exponential loss function as an asymmetric loss function. Also, Bayesian prediction (point and bounds) is considered for a future observation based on Type-II censored under two samples prediction. Numerical studies are given and some interesting comparisons are presented to illustrate the theoretical results. Moreover, the results are applied to real data sets.

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