Consistency rates and asymptotic normality of the high risk conditional for functional data

被引：0

作者：

Rabhi, Abbes ^{[1
]}

Keddani, Latifa ^{[2
]}

Hammon, Yassine ^{[1
]}

机构：

[1] Sidi Bel Abbes Univ, Math Lab, Sidi Bel Abbes, Algeria

[2] Moulay Tahar Univ Saida, Stochast Models Stat & Applicat Lab, Saida, Algeria

来源：

ACTA UNIVERSITATIS SAPIENTIAE-MATHEMATICA | 2016年 / 8卷 / 01期

关键词：

almost complete convergence; asymptotic normality; conditional hazard function; functional data; nonparametric estimation;

D O I：

10.1515/ausm-2016-0008

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

The maximum of the conditional hazard function is a parameter of great importance in seismicity studies, because it constitutes the maximum risk of occurrence of an earthquake in a given interval of time. Using the kernel nonparametric estimates of the first derivative of the conditional hazard function, we establish uniform convergence properties and asymptotic normality of an estimate of the maximum in the context of independence data.

引用

页码：127 / 149

页数：23

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