Book reviews. - Helmut Wegmann; R. Horst; J. Falkinger; Gerhard E. Tutz; Hans Laux; Heinz W. Engl; Anatol Rapoport (Z. Oper. Research, 1985)

Smoothed additive estimators for non-error rates in multiple discriminant analysis. - Gerhard E. Tutz (Pattern Recognit., 1985)

Alternative parameterizations in business tendency surveys. - Bernd Morawitz; Gerhard Tutz 001 (ZOR Methods Model. Oper. Res., 1990)

Log-linear parameterizations in discriminant analysis. - Gerhard Tutz 001 (ZOR Methods Model. Oper. Res., 1990)

Discrete kernels, loss functions and parametric models in discrete discrimination: A comparative study. - Gerhard Tutz 001; Herbert Groß (Math. Methods Oper. Res., 1995)

A CART-based approach to discover emerging patterns in microarray data. - Anne-Laure Boulesteix; Gerhard Tutz 001; Korbinian Strimmer (Bioinform., 2003)

Generalized linear random effects models with varying coefficients. - Gerhard Tutz 001; Göran Kauermann (Comput. Stat. Data Anal., 2003)

Exploring Multivariate Data Structures with Local Principal Curves. - Jochen Einbeck; Gerhard Tutz 001; Ludger Evers (GfKl, 2004)

Bagging, Boosting and Ordinal Classification. - Klaus Hechenbichler; Gerhard Tutz 001 (GfKl, 2004)

Generalized semiparametrically structured mixed models. - Gerhard Tutz 001 (Comput. Stat. Data Anal., 2004)

Local principal curves. - Jochen Einbeck; Gerhard Tutz 001; Ludger Evers (Stat. Comput., 2005)

Localized classification. - Gerhard Tutz 001; Harald Binder (Stat. Comput., 2005)

A Boosting Approach to Generalized Monotonic Regression. - Florian Leitenstorfer; Gerhard Tutz 001 (GfKl, 2006)

Identification of interaction patterns and classification with applications to microarray data. - Anne-Laure Boulesteix; Gerhard Tutz 001 (Comput. Stat. Data Anal., 2006)

Response shrinkage estimators in binary regression. - Gerhard Tutz 001; Florian Leitenstorfer (Comput. Stat. Data Anal., 2006)

Genetic algorithms for the selection of smoothing parameters in additive models. - Rüdiger Krause; Gerhard Tutz 001 (Comput. Stat., 2006)

Knot selection by boosting techniques. - Florian Leitenstorfer; Gerhard Tutz 001 (Comput. Stat. Data Anal., 2007)

Boosting ridge regression. - Gerhard Tutz 001; Harald Binder (Comput. Stat. Data Anal., 2007)

A comparison of methods for the fitting of generalized additive models. - Harald Binder; Gerhard Tutz 001 (Stat. Comput., 2008)

Supervised feature selection in mass spectrometry-based proteomic profiling by blockwise boosting. - Jan Gertheiss; Gerhard Tutz 001 (Bioinform., 2009)

Boosting nonlinear additive autoregressive time series. - Nivien Shafik; Gerhard Tutz 001 (Comput. Stat. Data Anal., 2009)

Penalized regression with correlation-based penalty. - Gerhard Tutz 001; Jan Ulbricht (Stat. Comput., 2009)

Guest Editorial: Regularisation Methods in Regression and Classification. - Gerhard Tutz 001 (Stat. Comput., 2010)

Poisson Regression. - Gerhard Tutz 001 (International Encyclopedia of Statistical Science, 2011)

Nonparametric estimation of the link function including variable selection. - Gerhard Tutz 001; Sebastian Petry (Stat. Comput., 2012)

Multinomial logit models with implicit variable selection. - Faisal Maqbool Zahid; Gerhard Tutz 001 (Adv. Data Anal. Classif., 2013)

Ridge estimation for multinomial logit models with symmetric side constraints. - Faisal Maqbool Zahid; Gerhard Tutz 001 (Comput. Stat., 2013)

Regularization and Model Selection with Categorical Covariates. - Jan Gertheiss; Veronika Stelz; Gerhard Tutz 001 (Algorithms from and for Nature and Life, 2013)

Variable selection for generalized linear mixed models by L 1-penalized estimation. - Andreas Groll; Gerhard Tutz 001 (Stat. Comput., 2014)

Variable selection in general multinomial logit models. - Gerhard Tutz 001; Wolfgang Pößnecker; Lorenz Uhlmann (Comput. Stat. Data Anal., 2015)

Improved methods for the imputation of missing data by nearest neighbor methods. - Gerhard Tutz 001; Shahla Ramzan (Comput. Stat. Data Anal., 2015)

Random forest for ordinal responses: Prediction and variable selection. - Silke Janitza; Gerhard Tutz 001; Anne-Laure Boulesteix (Comput. Stat. Data Anal., 2016)

Comparison of Maximum Likelihood with Conditional Pairwise Likelihood Estimation of Person Parameters in the Rasch Model. - Clemens Draxler; Gerhard Tutz 001; Katharina Zink; Can Gürer (Commun. Stat. Simul. Comput., 2016)

Additive mixed models with approximate Dirichlet process mixtures: the EM approach. - Felix Heinzl; Gerhard Tutz 001 (Stat. Comput., 2016)

Improved nearest neighbor classifiers by weighting and selection of predictors. - Gerhard Tutz 001; Dominik Koch (Stat. Comput., 2016)

A uniform framework for the combination of penalties in generalized structured models. - Margret-Ruth Oelker; Gerhard Tutz 001 (Adv. Data Anal. Classif., 2017)

Mixture models for ordinal responses to account for uncertainty of choice. - Gerhard Tutz 001; Micha Schneider; Maria Iannario; Domenico Piccolo (Adv. Data Anal. Classif., 2017)

Tree-structured modelling of categorical predictors in generalized additive regression. - Gerhard Tutz 001; Moritz Berger (Adv. Data Anal. Classif., 2018)

Tree-structured modelling of varying coefficients. - Moritz Berger; Gerhard Tutz 001; Matthias Schmid (Stat. Comput., 2019)

Comments on The class of cub models: statistical foundations, inferential issues and empirical evidence by D. Piccolo and R. Simone. - Gerhard Tutz 001 (Stat. Methods Appl., 2019)

Modelling heterogeneity: on the problem of group comparisons with logistic regression and the potential of the heterogeneous choice model. - Gerhard Tutz 001 (Adv. Data Anal. Classif., 2020)

Imputation methods for high-dimensional mixed-type datasets by nearest neighbors. - Shahla Faisal; Gerhard Tutz 001 (Comput. Biol. Medicine, 2021)

Multiple imputation using nearest neighbor methods. - Shahla Faisal; Gerhard Tutz 001 (Inf. Sci., 2021)

Tree-structured scale effects in binary and ordinal regression. - Gerhard Tutz 001; Moritz Berger (Stat. Comput., 2021)

Ordinal Trees and Random Forests: Score-Free Recursive Partitioning and Improved Ensembles. - Gerhard Tutz 001 (J. Classif., 2022)

Nearest neighbor imputation for categorical data by weighting of attributes. - Shahla Faisal; Gerhard Tutz 001 (Inf. Sci., 2022)

Probability and non-probability samples: Improving regression modeling by using data from different sources. - Gerhard Tutz 001 (Inf. Sci., 2023)