@book{KonakovLaeuterLiero1995, author = {Konakov, Valentin S. and L{\"a}uter, Henning and Liero, Hannelore}, title = {Comparison of the asymptotic power of tests based on L2- and L-norms under non-standard local alternatives}, series = {Discussion Paper / Humboldt-Universit{\"a}t zu Berlin, Institut f{\"u}r Mathematik, SFB 373}, volume = {10}, journal = {Discussion Paper / Humboldt-Universit{\"a}t zu Berlin, Institut f{\"u}r Mathematik, SFB 373}, address = {Berlin}, year = {1995}, language = {en} } @article{LieroLaeuterKonakov1998, author = {Liero, Hannelore and L{\"a}uter, Henning and Konakov, V. D.}, title = {Nonparametric versus parametric goodness of fit}, issn = {0323-3944}, year = {1998}, language = {en} } @unpublished{Liero2003, author = {Liero, Hannelore}, title = {Goodness of Fit Tests of L2-Type}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-51494}, year = {2003}, abstract = {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.}, language = {en} } @article{WichitsaNguanLaeuterLiero2016, author = {Wichitsa-Nguan, Korakot and L{\"a}uter, Henning and Liero, Hannelore}, title = {Estimability in Cox models}, series = {Statistical Papers}, volume = {57}, journal = {Statistical Papers}, publisher = {Springer}, address = {New York}, issn = {0932-5026}, doi = {10.1007/s00362-016-0755-x}, pages = {1121 -- 1140}, year = {2016}, abstract = {Our procedure of estimating is the maximum partial likelihood estimate (MPLE) which is the appropriate estimate in the Cox model with a general censoring distribution, covariates and an unknown baseline hazard rate . We find conditions for estimability and asymptotic estimability. The asymptotic variance matrix of the MPLE is represented and properties are discussed.}, language = {en} } @phdthesis{Liero1999, author = {Liero, Hannelore}, title = {Global measures of deviation of nonparametric curve estimators : strong uniform consistency and limit theorems}, address = {Potsdam}, pages = {305 S.}, year = {1999}, language = {en} } @book{Liero1999, author = {Liero, Hannelore}, title = {Testing homoscedasticity in nonparametric regression}, series = {Preprint / Universit{\"a}t Potsdam, Institut f{\"u}r Mathematik}, volume = {1999, 10}, journal = {Preprint / Universit{\"a}t Potsdam, Institut f{\"u}r Mathematik}, publisher = {Univ.}, address = {Potsdam}, year = {1999}, language = {en} } @unpublished{Liero2003, author = {Liero, Hannelore}, title = {Testing the Hazard Rate, Part I}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-51510}, year = {2003}, abstract = {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.}, language = {en} } @article{MuehlenbruchKuxhausPencinaetal.2015, author = {M{\"u}hlenbruch, Kristin and Kuxhaus, Olga and Pencina, Michael J. and Boeing, Heiner and Liero, Hannelore and Schulze, Matthias Bernd}, title = {A confidence ellipse for the Net Reclassification Improvement}, series = {European journal of epidemiology}, volume = {30}, journal = {European journal of epidemiology}, number = {4}, publisher = {Springer}, address = {Dordrecht}, issn = {0393-2990}, doi = {10.1007/s10654-015-0001-1}, pages = {299 -- 304}, year = {2015}, abstract = {The Net Reclassification Improvement (NRI) has become a popular metric for evaluating improvement in disease prediction models through the past years. The concept is relatively straightforward but usage and interpretation has been different across studies. While no thresholds exist for evaluating the degree of improvement, many studies have relied solely on the significance of the NRI estimate. However, recent studies recommend that statistical testing with the NRI should be avoided. We propose using confidence ellipses around the estimated values of event and non-event NRIs which might provide the best measure of variability around the point estimates. Our developments are illustrated using practical examples from EPIC-Potsdam study.}, language = {en} } @misc{MuehlenbruchKuxhausPencinaetal.2015, author = {M{\"u}hlenbruch, Kristin and Kuxhaus, Olga and Pencina, Michael J. and Boeing, Heiner and Liero, Hannelore and Schulze, Matthias Bernd}, title = {A confidence ellipse for the Net Reclassification Improvement}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch Naturwissenschaftliche Reihe}, number = {825}, issn = {1866-8372}, doi = {10.25932/publishup-42737}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-427371}, pages = {299 -- 304}, year = {2015}, abstract = {The Net Reclassification Improvement (NRI) has become a popular metric for evaluating improvement in disease prediction models through the past years. The concept is relatively straightforward but usage and interpretation has been different across studies. While no thresholds exist for evaluating the degree of improvement, many studies have relied solely on the significance of the NRI estimate. However, recent studies recommend that statistical testing with the NRI should be avoided. We propose using confidence ellipses around the estimated values of event and non-event NRIs which might provide the best measure of variability around the point estimates. Our developments are illustrated using practical examples from EPIC-Potsdam study.}, language = {en} }