Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6869786 | Computational Statistics & Data Analysis | 2014 | 17 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Pathé Ndao, Aliou Diop, Jean-François Dupuy,