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The scaled α-Winsorized estimate of exponential scale for censored data: an analysis based on two influence functions

✍ Scribed by Mara Tableman; Alix I. Gitelman

Publisher
Elsevier Science
Year
2002
Tongue
English
Weight
137 KB
Volume
59
Category
Article
ISSN
0167-7152

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✦ Synopsis

A new view of the maximum likelihood estimator (MLE) of exponential scale for censored data is presented. This is done by adapting Reid's (Ann. Statist. 9 (1981) 78) approach for obtaining the two in uence functions (IF) for the Kaplan-Meier estimate of the survival function; one for uncensored and one for censored data, respectively. The MLEs two IFs are derived. Via this analysis, we propose a new robust estimator, the scaled -Winsorized estimator (WE). Under Type II censoring, the WE is the MLE and, hence, is asymptotically e cient in that case. Its two IFs are bounded; hence,WE is B-robust. Its breakdown point is . A comparison is made with respect to asymptotic bias and mean square error at contaminated exponential and Weibull survival models.

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✍ Mohamed T. Madi 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 87 KB

We consider the problem of estimating the scale parameter  of the shifted exponential distribution with unknown shift based on a doubly censored sample from this distribution. Under squared error loss, Elfessi (Statist. Probab. Lett. 36 (1997) 251) has shown that the best a ne equivariant estimator