<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

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

Laboratory of Mathematics, Sidi Bel Abbes University

Stochastic Models Statistics and Applications Laboratory, Moulay Tahar University of Saida

Acta Universitatis Sapientiae, Mathematica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The maximum of the conditional hazard function is a parameter of great importance in seismicity studies, because it constitutes the maximum risk of occurrence...

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

Published Online: Aug 02, 2016

Page range: 127 - 149

Received: Nov 11, 2015

DOI: https://doi.org/10.1515/ausm-2016-0008

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

© 2016 Abbes Rabhi et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Published Online: Aug 02, 2016

Page range: 127 - 149

Received: Nov 11, 2015

DOI: https://doi.org/10.1515/ausm-2016-0008

Keywordsalmost complete convergence, asymptotic normality, conditional hazard function, functional data, nonparametric estimation

© 2016 Abbes Rabhi et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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