Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (1): 206-223.doi: 10.21629/JSEE.2020.01.20

• Reliability • Previous Articles     Next Articles

A statistical inference for generalized Rayleigh model under Type-Ⅱ progressive censoring with binomial removals

Junru REN(), Wenhao GUI*()   

  • Received:2019-03-18 Online:2020-02-20 Published:2020-02-25
  • Contact: Wenhao GUI;
  • About author:REN Junru was born in 1996. She received her bachelor's degree from School of Science, Beijing Jiaotong University. Now, she is studying for her graduate degree in statistics of Department of Mathematics, Beijing Jiaotong University. Her research interests are lifetime data analysis, lifetime distributions study and reliability theory. E-mail:|GUI Wenhao was born in l976. He received his B.S. and M.S. degrees from Beijing Jiaotong University in 1997 and 2000, respectively, and his Ph.D. degree from Florida State University, Tallahassee, USA in 2009. He has been a professor in the Department of Mathematics at Beijing Jiaotong University since 2014. His main research interests include data analysis, reliability analysis, distribution theory and parameter estimation. E-mail:
  • Supported by:
    the National Statistical Science Research Project of China(2019LZ32);This work was supported by the National Statistical Science Research Project of China (2019LZ32)


This paper considers the parameters and reliability characteristics estimation problem of the generalized Rayleigh distribution under progressively Type-Ⅱ censoring with random removals, that is, the number of units removed at each failure time follows the binomial distribution. The maximum likelihood estimation and the Bayesian estimation are derived. In the meanwhile, through a great quantity of Monte Carlo simulation experiments we have studied different hyperparameters as well as symmetric and asymmetric loss functions in the Bayesian estimation procedure. A real industrial case is presented to justify and illustrate the proposed methods. We also investigate the expected experimentation time and discuss the influence of the parameters on the termination point to complete the censoring test.

Key words: Type-Ⅱ progressive censoring with random removals, generalized Rayleigh distribution, reliability characteristic, maximum likelihood estimation, Markov chain Monte Carlo method, expected experimentation time