TY - JOUR A1 - Wichitsa-Nguan, Korakot A1 - Läuter, Henning A1 - Liero, Hannelore T1 - Estimability in Cox models JF - Statistical Papers N2 - 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. KW - Cox model KW - Estimability KW - Asymptotic variance of maximum partial likelihood estimate Y1 - 2016 U6 - https://doi.org/10.1007/s00362-016-0755-x SN - 0932-5026 SN - 1613-9798 VL - 57 SP - 1121 EP - 1140 PB - Springer CY - New York ER - TY - BOOK A1 - Läuter, Henning A1 - Sachsenweger, Cornelia T1 - Comparison of nonparametric goodness of fit tests T3 - Discussion paper / Humboldt-Universität zu Berlin, SFB 373, Quanifikation und Simulation Ökonomische Y1 - 1999 PB - Humboldt-Univ. CY - Berlin ER - TY - INPR A1 - Läuter, Henning A1 - Ramadan, Ayad T1 - Modeling and Scaling of Categorical Data N2 - Estimation and testing of distributions in metric spaces are well known. R.A. Fisher, J. Neyman, W. Cochran and M. Bartlett achieved essential results on the statistical analysis of categorical data. In the last 40 years many other statisticians found important results in this field. Often data sets contain categorical data, e.g. levels of factors or names. There does not exist any ordering or any distance between these categories. At each level there are measured some metric or categorical values. We introduce a new method of scaling based on statistical decisions. For this we define empirical probabilities for the original observations and find a class of distributions in a metric space where these empirical probabilities can be found as approximations for equivalently defined probabilities. With this method we identify probabilities connected with the categorical data and probabilities in metric spaces. Here we get a mapping from the levels of factors or names into points of a metric space. This mapping yields the scale for the categorical data. From the statistical point of view we use multivariate statistical methods, we calculate maximum likelihood estimations and compare different approaches for scaling. T3 - Mathematische Statistik und Wahrscheinlichkeitstheorie : Preprint - 2010, 03 Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-49572 ER - TY - INPR A1 - Läuter, Henning A1 - Ramadan, Ayad T1 - Statistical Scaling of Categorical Data N2 - Estimation and testing of distributions in metric spaces are well known. R.A. Fisher, J. Neyman, W. Cochran and M. Bartlett achieved essential results on the statistical analysis of categorical data. In the last 40 years many other statisticians found important results in this field. Often data sets contain categorical data, e.g. levels of factors or names. There does not exist any ordering or any distance between these categories. At each level there are measured some metric or categorical values. We introduce a new method of scaling based on statistical decisions. For this we define empirical probabilities for the original observations and find a class of distributions in a metric space where these empirical probabilities can be found as approximations for equivalently defined probabilities. With this method we identify probabilities connected with the categorical data and probabilities in metric spaces. Here we get a mapping from the levels of factors or names into points of a metric space. This mapping yields the scale for the categorical data. From the statistical point of view we use multivariate statistical methods, we calculate maximum likelihood estimations and compare different approaches for scaling. T3 - Mathematische Statistik und Wahrscheinlichkeitstheorie : Preprint - 2010, 01 Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-49566 ER - TY - BOOK A1 - Läuter, Henning A1 - Nikulin, Mikhail S. T1 - Parametric versus nonparametric goodness of fit : another view T3 - Discussion paper / Humboldt-Universität zu Berlin, SFB 373, Quantifikation und Simulatio Y1 - 1999 PB - Humboldt-Univ. CY - Berlin ER - TY - BOOK A1 - Läuter, Henning A1 - Liero, Hannelore T1 - Ill-posed inverse problems T3 - Discussion Paper / Humboldt-Universität zu Berlin, Institut für Mathematik, SFB 373 Y1 - 1996 CY - Berlin ER - TY - INPR A1 - Läuter, Henning A1 - Liero, Hannelore T1 - Nonparametric estimation and testing in survival models N2 - The aim of this paper is to demonstrate that nonparametric smoothing methods for estimating functions can be an useful tool in the analysis of life time data. After stating some basic notations we will present a data example. Applying standard parametric methods to these data we will see that this approach fails - basic features of the underlying functions are not reflected by their estimates. Our proposal is to use nonparametric estimation methods. These methods are explained in section 2. Nonparametric approaches are better in the sense that they are more flexible, and misspecifications of the model are avoided. But, parametric models have the advantage that the parameters can be interpreted. So, finally, we will formulate a test procedure to check whether a parametric or a nonparametric model is appropriate. T3 - Mathematische Statistik und Wahrscheinlichkeitstheorie : Preprint - 2004, 05 Y1 - 2004 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-51586 ER - TY - BOOK A1 - Läuter, Henning T1 - Estimation in partly parametric additive Cox models T3 - Preprint / Universität Potsdam, Institut für Mathematik, Arbeitsgruppe Partiell Y1 - 2003 SN - 1437-739X PB - Univ. CY - Potsdam ER - TY - JOUR A1 - Läuter, Henning T1 - Nonparametric versus parametric goodness of fit Y1 - 1996 ER - TY - JOUR A1 - Läuter, Henning T1 - Nonlinear estimation problems Y1 - 1999 ER -