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A Review on the Analysis of Life Data Based on Bayesian Method: 2000~2016  

Won, Dong-Yeon (Dept. of Industrial and Management Engineering, Kyonggi University Graduate School)
Lim, Jun Hyoung (Dept. of Industrial and Management Engineering, Kyonggi University Graduate School)
Sim, Hyun Su (Dept. of Industrial and Management Engineering, Kyonggi University Graduate School)
Sung, Si-il (Dept. of Industrial and Management Engineering, Inje University)
Lim, Heonsang (Division of Quality Assurance, Samsung Electronics)
Kim, Yong Soo (Dept. of Industrial and Management Engineering, Kyonggi University)
Publication Information
Journal of Applied Reliability / v.17, no.3, 2017 , pp. 213-223 More about this Journal
Abstract
Purpose: The purpose of this study is to arrange the life data analysis literatures based on the Bayesian method quantitatively and provide it as tables. Methods: The Bayesian method produces a more accurate estimates of other traditional methods in a small sample size, and it requires specific algorithm and prior information. Based on these three characteristics of the Bayesian method, the criteria for classifying the literature were taken into account. Results: In many studies, there are comparisons of estimation methods for the Bayesian method and maximum likelihood estimation (MLE), and sample size was greater than 10 and not more than 25. In probability distributions, a variety of distributions were found in addition to the distributions of Weibull commonly used in life data analysis, and MCMC and Lindley's Approximation were used evenly. Finally, Gamma, Uniform, Jeffrey and extension of Jeffrey distributions were evenly used as prior information. Conclusion: To verify the characteristics of the Bayesian method which are more superior to other methods in a smaller sample size, studies in less than 10 samples should be carried out. Also, comparative study is required by various distributions, thereby providing guidelines necessary.
Keywords
Bayesian Method; Life Data; Sample Size; MCMC(Markov chain Monte Carlo); Lindley's Approximation;
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Times Cited By KSCI : 6  (Citation Analysis)
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