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

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

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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 func...

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Bibliographic Details
Main Authors: Rabhi Abbes, Keddani Latifa, Hammou Yassine
Format: Article
Language:English
Published: Sciendo 2015-12-01
Series:Acta Universitatis Sapientiae: Mathematica
Subjects:
almost complete convergence
asymptotic normality
conditional hazard function
functional data
nonparametric estimation
Online Access:https://doi.org/10.1515/ausm-2015-0015
Description
Summary: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.
ISSN:2066-7752

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