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

Takeshi Emura and Shau-Kai Shiu

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Akaike's information criterion; EM algorithm; lognormal distribution; Newton-Raphson algorithm; Weibull distribution; Reliability (search for similar items in EconPapers)
JEL-codes: C34 (search for similar items in EconPapers)
Date: 2014-07-24
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

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