Robust conditional Weibull-type estimation
We study nonparametric robust tail coefficient estimation when the variable of interest, assumed to be of Weibull type, is observed simultaneously with a random covariate. In particular, we introduce a robust estimator for the tail coefficient, using the idea of the density power divergence, based o...
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| Veröffentlicht in: | Annals of the Institute of Statistical Mathematics 2015-06, Vol.67 (3), p.479-514 |
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| container_title | Annals of the Institute of Statistical Mathematics |
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| creator | Goegebeur, Yuri Guillou, Armelle Rietsch, Théo |
| description | We study nonparametric robust tail coefficient estimation when the variable of interest, assumed to be of Weibull type, is observed simultaneously with a random covariate. In particular, we introduce a robust estimator for the tail coefficient, using the idea of the density power divergence, based on the relative excesses above a high threshold. The main asymptotic properties of our estimator are established under very general assumptions. The finite sample performance of the proposed procedure is evaluated by a small simulation experiment. |
| doi_str_mv | 10.1007/s10463-014-0458-9 |
| format | Article |
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In particular, we introduce a robust estimator for the tail coefficient, using the idea of the density power divergence, based on the relative excesses above a high threshold. The main asymptotic properties of our estimator are established under very general assumptions. 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| subjects | Asymptotic properties Coefficients Density Economics Estimating techniques Estimators Finance Insurance Management Mathematical analysis Mathematics Mathematics and Statistics Maximum likelihood method Normal distribution Samples Simulation Statistical analysis Statistics Statistics for Business Studies |
| title | Robust conditional Weibull-type estimation |
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