Nonparametric kernel estimation of Weibull-tail coefficient in presence\n of the right random censoring

2021/10/10 by Justin Ushize Rutikanga, Rutikange, Justin Ushize, Aliou Diop +1
Decision Sciences · Environmental Science · Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Hydrology and Drought Analysis #Methodology (stat.ME) #Probabilistic and Robust Engineering Design #Statistical Distribution Estimation and Applications #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.2110.04772

openalex publication_date 2021/10/10 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28

Abstract

In this paper, nonparametric estimation of the conditional Weibull-tail\ncoefficient when the variable of interest is right random censored is\naddressed. A Weissman-type estimator of conditional extreme quantile is also\nproposed. In addition, a simulation study is conducted to assess the\nfinite-sample behavior of the proposed estimators and a comparison with\nalternative strategies is provided. Finally, the practical applicability of the\nmethodology is presented using a real datasets of men suffering from a larynx\ncancer.\n

Citations

• Limiting forms of the frequency distribution of the largest or smallest member of a sample

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