Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
computer science
Nonparametric estimation of the conditional tail index and extreme quantiles under random censoring
Computational Statistics and Data Analysis, Volume 79, Year 2014
Notification
URL copied to clipboard!
Description
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. © 2014 Elsevier B.V. All rights reserved.
Authors & Co-Authors
Ndao, Pathé
Senegal, Saint-louis
Université Gaston Berger de Saint-louis
Diop, Aliou
Senegal, Saint-louis
Université Gaston Berger de Saint-louis
Dupuy, Jean François
France, Rennes
Institut de Recherche Mathématique de Rennes
Statistics
Citations: 32
Authors: 3
Affiliations: 2
Identifiers
Doi:
10.1016/j.csda.2014.05.007
ISSN:
01679473