{"ID":2892339,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.15744","arxiv_id":"2507.15744","title":"Robust and Smooth Estimation of the Extreme Tail Index via Weighted Minimum Density Power Divergence","abstract":"By introducing a weight function into the density power divergence, we develop a new class of robust and smooth estimators for the tail index of Pareto-type distributions, offering improved efficiency in the presence of outliers. These estimators can be viewed as a robust generalization of both weighted least squares and kernel-based tail index estimators. We establish the consistency and asymptotic normality of the proposed class. A simulation study is conducted to assess their finite-sample performance in comparison with existing methods.","short_abstract":"By introducing a weight function into the density power divergence, we develop a new class of robust and smooth estimators for the tail index of Pareto-type distributions, offering improved efficiency in the presence of outliers. These estimators can be viewed as a robust generalization of both weighted least squares a...","url_abs":"https://arxiv.org/abs/2507.15744","url_pdf":"https://arxiv.org/pdf/2507.15744v2","authors":"[\"Saida Mancer\",\"Abdelhakim Necir\",\"Djamel Meraghni\"]","published":"2025-07-21T15:58:52Z","proceeding":"math.ST","tasks":"[\"math.ST\"]","methods":"[]","has_code":false}