On statistics of extremes under random censoring
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Date
2016-04-07
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Abstract
In the field of statistics of extremes, the most common assumption is to consider that samples are independent and identically distributed or weakly dependent and stationary from a distribution function F. However, in most real life applications such as survival analysis and reliability data, observations are usually censored. For such data sets, the classical estimators in statistics of extremes need some modification to take into account censoring. Compared to the classical statistics of extremes, the case of censoring is fairly new and in its early stages of development. In this presentation, we propose some estimators of the Extreme Value Index and extreme quantiles when data is randomly censored. We compare the proposed estimators with an exhaustive list of estimators in the literature in a large simulation study. The results show that our estimators are more robust to censoring. In addition, the estimators provide better confidence intervals when there is heavy censoring and more intermediate observations are included in the estimation.
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Seminar
Keywords
Extreme value index, extreme quantiles, confidence intervals, censoring schemes, bootstrap