Bayesian variable sampling plan for the weibull distribution with type I censoring

1999 ◽  
Vol 15 (3) ◽  
pp. 269-280 ◽  
Author(s):  
Chen Jianwei ◽  
Lam Yeh
Keyword(s):  
Weibull Distribution ◽  
Sampling Plan ◽  
Type I ◽  
Type I Censoring ◽  
Variable Sampling

scholarly journals Absolute Deviation based Control Charts for Monitoring Mean of Weibull Distribution with Type-I Censoring

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sajid Ali ◽  
Nabeel Ahmed ◽  
Ismail Shah ◽  
Showkat Ahmad Lone ◽  
Abdulaziz Q. Alsubie
Keyword(s):  
Weibull Distribution ◽  
Control Charts ◽  
Type I ◽  
Absolute Deviation ◽  
Type I Censoring

Comment on “Exact Bayesian variable sampling plans for the exponential distribution based on type-I hybrid censored samples” and “its corrections”

2013 ◽  
Vol 44 (2) ◽  
pp. 113-122
Author(s):  
Tachen Liang
Keyword(s):  
Exponential Distribution ◽  
Sampling Plan ◽  
Design Parameters ◽  
Type I ◽  
Sampling Plans ◽  
Bayesian Sampling ◽  
Censored Samples ◽  
Variable Sampling

We compare the performances of two sampling plans, namely, the Lin-Liang-Huang (2002)'s Bayesian sampling plan $(n^*,\xi^*)$ and the Lin-Huang-Balakrishnan (2008a, 2010a)'s exact Bayesian sampling plan $(n_0,r_0,t_0,\xi_0)$. We also comment the accuracy of the values of the design parameters $(n_0,r_0,t_0,\xi_0)$ provided in Lin-Huang-Balakrishnan (2010a). We conclude that among the class of sampling plans $(n,r,t,\xi)$ of Lin et al.~(2008a, 2010a), the exact Bayesian sampling plan does not exist.

Optimum simple ramp-tests for the Weibull distribution and type-I censoring

1992 ◽  
Vol 41 (3) ◽  
pp. 407-413 ◽  
Author(s):  
D.S. Bai ◽  
M.S. Cha ◽  
S.W. Chung
Keyword(s):  
Weibull Distribution ◽  
Type I ◽  
Type I Censoring

scholarly journals Bayesian Modeling of 3-Component Mixture of Exponentiated Inverted Weibull Distribution under Noninformative Prior

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Ammara Nawaz Cheema ◽  
Muhammad Aslam ◽  
Ibrahim M. Almanjahie ◽  
Ishfaq Ahmad
Keyword(s):  
Weibull Distribution ◽  
Research Work ◽  
Real Data ◽  
Loss Functions ◽  
Type I ◽  
Bayes Estimators ◽  
Component Mixture ◽  
Type I Censoring ◽  
Censoring Technique ◽  
The Impact

Bayesian study of 3-component mixture modeling of exponentiated inverted Weibull distribution under right type I censoring technique is conducted in this research work. The posterior distribution of the parameters is obtained assuming the noninformative (Jeffreys and uniform) priors. The different loss functions (squared error, quadratic, precautionary, and DeGroot loss function) are used to obtain the Bayes estimators and posterior risks. The performance of the Bayes estimators through posterior risks under the said loss functions is investigated through simulation process. Real data analysis of tensile strength of carbon fiber is also applied for 3 components to conclude the presentation of Bayes estimators. The limiting expressions are also elaborated for Bayes estimators and posterior risks in this study. The impact of some test termination times and sample sizes is reported on Bayes estimators.

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