Testing the Hazard Rate, Part I

  • We consider a nonparametric survival model with random censoring. To test whether the hazard rate has a parametric form the unknown hazard rate is estimated by a kernel estimator. Based on a limit theorem stating the asymptotic normality of the quadratic distance of this estimator from the smoothed hypothesis an asymptotic ®-test is proposed. Since the test statistic depends on the maximum likelihood estimator for the unknown parameter in the hypothetical model properties of this parameter estimator are investigated. Power considerations complete the approach.

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Author:Hannelore Liero
Series (Serial Number):Mathematische Statistik und Wahrscheinlichkeitstheorie : Preprint (2003, 17)
Document Type:Preprint
Year of Completion:2003
Publishing Institution:Universität Potsdam
Release Date:2011/03/28
Tag:censoring; goodness of fit; kernel estimator of the hazard rate; limit theorem for integrated squared difference; maximum likelihood estimator
RVK - Regensburg Classification:SI 990
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik
Licence (German):License LogoKeine Nutzungslizenz vergeben - es gilt das deutsche Urheberrecht