Competing Risks Model of Marshall-Olkin Bivariate Exponential Distribution Under Hybrid Censoring

• Main Authors: Hsiang-han Lin, 林香漢
• Other Authors: Tsai-hung Fan
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/92790688920899257632

碩士 === 國立中央大學 === 統計研究所 === 100 === In a competing risks model, the component fails if any of the risk factors fails. These factors are all from the same component, hence they may be correlated. In this thesis, we consider the competing risks model under bivariate Marshall-Olkin exponential distribution under hybrid censoring which is the mixture of conventional Type I and Type II censoring schemes and is quite useful in lifetesting or reliability experiments. It is often to include masked data in which the risk factor that causes failure of the component is not observed. We apply the maximum likelihood approach via EM algorithm along with the missing information principle to estimate the standard errors of the MLE. Bayesian approach incorporated with subjective prior and noninformative prior is also considered with the aid of MCMC method. Statistical inference on the model parameters as well as the mean lifetimes and the reliability functions of the component and risk factors is derived. Simulation study shows that the maximum likelihood approach performs poorly when the proportion of the masking data is high due to insufficient information, while Bayesian method can be provide good results with reliable prior information.