Estimation and model selection for left-truncated and right-censored lifetime data with application to electric power transformers analysis

Emura, Takeshi and Shiu, Shau-Kai (2014): Estimation and model selection for left-truncated and right-censored lifetime data with application to electric power transformers analysis. Forthcoming in: Communications in Statistics - Simulation and Computation

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 algorithm. In this paper, the first objective is a systematic comparison of the EM algorithm with the Newton-Raphson 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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Akaike's information criterion; EM algorithm; lognormal distribution; Newton-Raphson algorithm; Weibull distribution; Reliability
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C34 - Truncated and Censored Models ; Switching Regression Models

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