Bias-corrected estimation for conditional Pareto-type distributions with random right censoring

Abstract : We consider bias-reduced estimation of the extreme value index in conditional Pareto-type models with random covariates when the response variable is subject to random right censoring. The bias-correction is obtained by fitting the extended Pareto distribution locally to the relative excesses over a high threshold using the maximum likelihood method. Consistency and asymptotic normality of the estimators are established under suitable assumptions. The finite sample behaviour is illustrated with a small simulation experiment and the method is applied to AIDS survival data.
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https://hal.archives-ouvertes.fr/hal-01826112
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Submitted on : Friday, June 29, 2018 - 8:56:58 AM
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Yuri Goegebeur, Armelle Guillou, Jing Qin. Bias-corrected estimation for conditional Pareto-type distributions with random right censoring. 2018. 〈hal-01826112〉

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