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Article Dans Une Revue Journal of Statistical Planning and Inference Année : 2016

Nonparametric estimation of the conditional extreme-value index with random covariates and censoring

Résumé

Estimation of the extreme-value index of a heavy-tailed distribution is addressed when some random covariate information is available and the data are randomly right-censored. An inverse-probability-of-censoring-weighted kernel version of Hill's estimator of the extreme-value index is proposed and its asymptotic normality is established. Based on this, a Weissman-type estimator of conditional extreme quantiles is also constructed. A simulation study is conducted to assess the finite-sample behaviour of the proposed estimators.
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Dates et versions

hal-01056117 , version 1 (15-08-2014)
hal-01056117 , version 2 (06-09-2014)

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Citer

Pathé Ndao, Aliou Diop, Jean-François Dupuy. Nonparametric estimation of the conditional extreme-value index with random covariates and censoring. Journal of Statistical Planning and Inference, 2016, 168, pp.20-37. ⟨10.1016/j.jspi.2015.06.004⟩. ⟨hal-01056117v2⟩
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