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Assessing the lifetime performance index of products from progressively type II right censored data using Burr XII model

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  • Lee, Wen-Chuan
  • Wu, Jong-Wuu
  • Hong, Ching-Wen

Abstract

Process capability analysis has been widely applied in the field of quality control to monitor the performance of industrial processes. In practice, lifetime performance index CL is a popular means to assess the performance and potential of their processes, where L is the lower specification limit. Nevertheless, many processes possess a non-normal lifetime model, the assumption of normality is often erroneous. Progressively censoring scheme is quite useful in many practical situations where budget constraints are in place or there is a demand for rapid testing. The study will apply data transformation technology to constructs a maximum likelihood estimator (MLE) of CL under the Burr XII distribution based on the progressively type II right censored sample. The MLE of CL is then utilized to develop a new hypothesis testing procedure in the condition of known L. Finally, we give two examples to illustrate the use of the testing procedure under given significance level α.

Suggested Citation

  • Lee, Wen-Chuan & Wu, Jong-Wuu & Hong, Ching-Wen, 2009. "Assessing the lifetime performance index of products from progressively type II right censored data using Burr XII model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(7), pages 2167-2179.
    • Handle: RePEc:eee:matcom:v:79:y:2009:i:7:p:2167-2179
    DOI: 10.1016/j.matcom.2008.12.001
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    References listed on IDEAS

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    1. Li, Xiuchun & Shi, Yimin & Wei, Jieqiong & Chai, Jian, 2007. "Empirical Bayes estimators of reliability performances using LINEX loss under progressively Type-II censored samples," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 73(5), pages 320-326.
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    Cited by:

    1. Mohammad Vali Ahmadi & Jafar Ahmadi & Mousa Abdi, 2019. "Evaluating the lifetime performance index of products based on generalized order statistics from two-parameter exponential model," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(2), pages 251-275, April.
    2. Laumen Benjamin & Cramer Erhard, 2015. "Likelihood Inference for the Lifetime Performance Index under Progressive Type-II Censoring," Stochastics and Quality Control, De Gruyter, vol. 30(2), pages 59-73, December.
    3. Tzong-Ru Tsai & Hua Xin & Ya-Yen Fan & Yuhlong Lio, 2022. "Bias-Corrected Maximum Likelihood Estimation and Bayesian Inference for the Process Performance Index Using Inverse Gaussian Distribution," Stats, MDPI, vol. 5(4), pages 1-18, November.
    4. Jianping Zhu & Hua Xin & Chenlu Zheng & Tzong-Ru Tsai, 2021. "Inference for the Process Performance Index of Products on the Basis of Power-Normal Distribution," Mathematics, MDPI, vol. 10(1), pages 1-14, December.
    5. Mohammad Vali Ahmadi & Mahdi Doostparast & Jafar Ahmadi, 2015. "Statistical inference for the lifetime performance index based on generalised order statistics from exponential distribution," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(6), pages 1094-1107, April.

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