Mathematische Statistik und Wahrscheinlichkeitstheorie : Preprint
ISSN (print) 1613-3307
URN urn:nbn:de:kobv:517-series-317
Herausgegeben vom
Institut für Mathematik, Mathematische Statistik und Wahrscheinlichkeitstheorie
URN urn:nbn:de:kobv:517-series-317
Herausgegeben vom
Institut für Mathematik, Mathematische Statistik und Wahrscheinlichkeitstheorie
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2003, 17
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.