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.
2003, 16
The dependence between survival times and covariates is described e.g. by proportional hazard models. We consider partly parametric Cox models and discuss here the estimation of interesting parameters. We represent the ma- ximum likelihood approach and extend the results of Huang (1999) from linear to nonlinear parameters. Then we investigate the least squares esti- mation and formulate conditions for the a.s. boundedness and consistency of these estimators.
2003, 15
We give a survey on procedures for testing functions which are based on quadratic deviation measures. The following problems are considered: Testing whether a density function lies in a parametric class of functions, whether continuous random variables are independent; testing cell probabilities and independence in sparse data sets; testing the parametric fit of a regression homoscedasticity in a regression model and testing the hazard rate in survival models with censoring and with and without covariates.