Estimation and Model Selection for Left-truncated and Right-censored Lifetime Data with Application to Electric Power Transformers Analysis |
| |
Authors: | Takeshi Emura Shau-Kai Shiu |
| |
Institution: | Graduate Institute of Statistics, National Central University, Taoyuan City, Taiwan |
| |
Abstract: | In lifetime analysis of electric transformers, the maximum likelihood estimation has been proposed with the EM algorithm. However, it is not clear whether the EM algorithm offers a better solution compared to the simpler Newton-Raphson (NR) algorithm. In this article, the first objective is a systematic comparison of the EM algorithm with the NR algorithm in terms of convergence performance. The second objective is to examine the performance of Akaike's information criterion (AIC) for selecting a suitable distribution among candidate models via simulations. These methods are illustrated through the electric power transformer dataset. |
| |
Keywords: | Akaike’s information criterion EM algorithm Lognormal distribution Newton-Raphson algorithm Reliability Weibull distribution |
|