Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 79, Issue -, Pages 63-79Publisher
ELSEVIER
DOI: 10.1016/j.csda.2014.05.007
Keywords
Conditional extreme value index; Conditional extreme quantiles; Heavy-tailed distribution; Moving window; Simulations
Funding
- Agence Universitaire de la Francophonie
- French Ministry of Foreign and European Affairs [7168]
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The estimation of the tail index and extreme quantiles of a heavy-tailed distribution is addressed when some covariate information is available and the data are randomly right-censored. Several estimators are constructed by combining a moving-window technique (for tackling the covariate information) and the inverse probability-of-censoring weighting method. The asymptotic normality of these estimators is established and their finite-sample properties are investigated via simulations. A comparison with alternative estimators is provided. Finally, the proposed methodology is illustrated on a medical dataset. (C) 2014 Elsevier B.V. All rights reserved.
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