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Robust estimation of the Pickands dependence function under random right censoring.
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- Author(s): Goegebeur, Yuri1 (AUTHOR) ; Guillou, Armelle2 (AUTHOR); Qin, Jing1 (AUTHOR)
- Source:
Insurance: Mathematics & Economics. Jul2019, Vol. 87, p101-114. 14p.- Subject Terms:
- Source:
- Additional Information
- Abstract: We consider robust nonparametric estimation of the Pickands dependence function under random right censoring. The estimator is obtained by applying the minimum density power divergence criterion to properly transformed bivariate observations. The asymptotic properties are investigated by making use of results for Kaplan–Meier integrals. We investigate the finite sample properties of the proposed estimator with a simulation experiment and illustrate its practical applicability on a dataset of insurance indemnity losses. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Insurance: Mathematics & Economics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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