TY  - INPR
A1  - Läuter, Henning
T1  - Estimation in partly parametric additive Cox models
N2  - 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.
T3  - Mathematische Statistik und Wahrscheinlichkeitstheorie : Preprint - 2003, 16 
KW  - Survival models with covariates
KW  - estimation of regression
KW  - maximum likelihood estimator
KW  - least squares estimator
KW  - boun- dedness
KW  - consistency
Y1  - 2003
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-51509
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  - INPR
A1  - Läuter, Henning
T1  - On approximate likelihood in survival models
N2  - We give a common frame for different estimates in survival models. For models with nuisance parameters we approximate the profile likelihood and find estimates especially for the proportional hazard model.
T3  - Mathematische Statistik und Wahrscheinlichkeitstheorie : Preprint - 2006, 04 
KW  - Approximate likelihood
KW  - profile likelihood
KW  - propor-tional hazard mode
Y1  - 2006
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-51615
ER  - 
TY  - INPR
A1  - Läuter, Henning
T1  - Empirical Minimax Linear Estimates
N2  - We give the explicit solution for the minimax linear estimate. For scale dependent models an empirical minimax linear estimates is de¯ned and we prove that these estimates are Stein's estimates.
T3  - Mathematische Statistik und Wahrscheinlichkeitstheorie : Preprint - 2008, 06 
Y1  - 2008
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-49483
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  -