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Gerhard Tutz 0001
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- affiliation: Ludwig Maximilian University of Munic (LMU), Institute of Statistics, Germany
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2020 – today
- 2023
- [j42]Gerhard Tutz:
Probability and non-probability samples: Improving regression modeling by using data from different sources. Inf. Sci. 621: 424-436 (2023) - 2022
- [j41]Gerhard Tutz
:
Ordinal Trees and Random Forests: Score-Free Recursive Partitioning and Improved Ensembles. J. Classif. 39(2): 241-263 (2022) - [j40]Shahla Faisal, Gerhard Tutz:
Nearest neighbor imputation for categorical data by weighting of attributes. Inf. Sci. 592: 306-319 (2022) - 2021
- [j39]Shahla Faisal
Imputation methods for high-dimensional mixed-type datasets by nearest neighbors. Comput. Biol. Medicine 135: 104577 (2021) - [j38]Shahla Faisal, Gerhard Tutz:
Multiple imputation using nearest neighbor methods. Inf. Sci. 570: 500-516 (2021) - [j37]Gerhard Tutz, Moritz Berger
Tree-structured scale effects in binary and ordinal regression. Stat. Comput. 31(2): 17 (2021) - 2020
- [j36]Gerhard Tutz
Modelling heterogeneity: on the problem of group comparisons with logistic regression and the potential of the heterogeneous choice model. Adv. Data Anal. Classif. 14(3): 517-542 (2020)
2010 – 2019
- 2019
- [j35]Moritz Berger
Tree-structured modelling of varying coefficients. Stat. Comput. 29(2): 217-229 (2019) - [j34]Gerhard Tutz
Comments on The class of cub models: statistical foundations, inferential issues and empirical evidence by D. Piccolo and R. Simone. Stat. Methods Appl. 28(3): 471-475 (2019) - 2018
- [j33]Gerhard Tutz, Moritz Berger
Tree-structured modelling of categorical predictors in generalized additive regression. Adv. Data Anal. Classif. 12(3): 737-758 (2018) - 2017
- [j32]Margret-Ruth Oelker, Gerhard Tutz:
A uniform framework for the combination of penalties in generalized structured models. Adv. Data Anal. Classif. 11(1): 97-120 (2017) - [j31]Gerhard Tutz, Micha Schneider, Maria Iannario
Mixture models for ordinal responses to account for uncertainty of choice. Adv. Data Anal. Classif. 11(2): 281-305 (2017) - 2016
- [j30]Silke Janitza
Random forest for ordinal responses: Prediction and variable selection. Comput. Stat. Data Anal. 96: 57-73 (2016) - [j29]Clemens Draxler, Gerhard Tutz, Katharina Zink, Can Gürer:
Comparison of Maximum Likelihood with Conditional Pairwise Likelihood Estimation of Person Parameters in the Rasch Model. Commun. Stat. Simul. Comput. 45(6): 2007-2017 (2016) - [j28]Felix Heinzl, Gerhard Tutz:
Additive mixed models with approximate Dirichlet process mixtures: the EM approach. Stat. Comput. 26(1-2): 73-92 (2016) - [j27]Gerhard Tutz, Dominik Koch:
Improved nearest neighbor classifiers by weighting and selection of predictors. Stat. Comput. 26(5): 1039-1057 (2016) - 2015
- [j26]Gerhard Tutz, Wolfgang Pößnecker, Lorenz Uhlmann:
Variable selection in general multinomial logit models. Comput. Stat. Data Anal. 82: 207-222 (2015) - [j25]Gerhard Tutz, Shahla Ramzan:
Improved methods for the imputation of missing data by nearest neighbor methods. Comput. Stat. Data Anal. 90: 84-99 (2015) - 2014
- [j24]Andreas Groll
Variable selection for generalized linear mixed models by L 1-penalized estimation. Stat. Comput. 24(2): 137-154 (2014) - 2013
- [j23]Faisal Maqbool Zahid, Gerhard Tutz:
Multinomial logit models with implicit variable selection. Adv. Data Anal. Classif. 7(4): 393-416 (2013) - [j22]Faisal Maqbool Zahid, Gerhard Tutz:
Ridge estimation for multinomial logit models with symmetric side constraints. Comput. Stat. 28(3): 1017-1034 (2013) - [p1]Jan Gertheiss, Veronika Stelz, Gerhard Tutz:
Regularization and Model Selection with Categorical Covariates. Algorithms from and for Nature and Life 2013: 215-222 - 2012
- [j21]Gerhard Tutz, Sebastian Petry:
Nonparametric estimation of the link function including variable selection. Stat. Comput. 22(2): 545-561 (2012) - 2011
- [r1]Gerhard Tutz:
Poisson Regression. International Encyclopedia of Statistical Science 2011: 1075-1077 - 2010
- [j20]Gerhard Tutz:
Guest Editorial: Regularisation Methods in Regression and Classification. Stat. Comput. 20(2): 117-118 (2010)
2000 – 2009
- 2009
- [j19]Jan Gertheiss, Gerhard Tutz:
Supervised feature selection in mass spectrometry-based proteomic profiling by blockwise boosting. Bioinform. 25(8): 1076-1077 (2009) - [j18]Nivien Shafik, Gerhard Tutz:
Boosting nonlinear additive autoregressive time series. Comput. Stat. Data Anal. 53(7): 2453-2464 (2009) - [j17]Gerhard Tutz, Jan Ulbricht:
Penalized regression with correlation-based penalty. Stat. Comput. 19(3): 239-253 (2009) - 2008
- [j16]Harald Binder
A comparison of methods for the fitting of generalized additive models. Stat. Comput. 18(1): 87-99 (2008) - 2007
- [j15]Florian Leitenstorfer, Gerhard Tutz:
Knot selection by boosting techniques. Comput. Stat. Data Anal. 51(9): 4605-4621 (2007) - [j14]Gerhard Tutz, Harald Binder
Boosting ridge regression. Comput. Stat. Data Anal. 51(12): 6044-6059 (2007) - 2006
- [j13]Anne-Laure Boulesteix
Identification of interaction patterns and classification with applications to microarray data. Comput. Stat. Data Anal. 50(3): 783-802 (2006) - [j12]Gerhard Tutz, Florian Leitenstorfer:
Response shrinkage estimators in binary regression. Comput. Stat. Data Anal. 50(10): 2878-2901 (2006) - [j11]Rüdiger Krause, Gerhard Tutz:
Genetic algorithms for the selection of smoothing parameters in additive models. Comput. Stat. 21(1): 9-31 (2006) - [c3]Florian Leitenstorfer, Gerhard Tutz:
A Boosting Approach to Generalized Monotonic Regression. GfKl 2006: 245-254 - 2005
- [j10]Gerhard Tutz, Harald Binder
Localized classification. Stat. Comput. 15(3): 155-166 (2005) - [j9]Jochen Einbeck, Gerhard Tutz, Ludger Evers:
Local principal curves. Stat. Comput. 15(4): 301-313 (2005) - 2004
- [j8]Gerhard Tutz:
Generalized semiparametrically structured mixed models. Comput. Stat. Data Anal. 46(4): 777-800 (2004) - [c2]Klaus Hechenbichler, Gerhard Tutz:
Bagging, Boosting and Ordinal Classification. GfKl 2004: 145-152 - [c1]Jochen Einbeck
Exploring Multivariate Data Structures with Local Principal Curves. GfKl 2004: 256-263 - 2003
- [j7]Anne-Laure Boulesteix
A CART-based approach to discover emerging patterns in microarray data. Bioinform. 19(18): 2465-2472 (2003) - [j6]Gerhard Tutz, Göran Kauermann:
Generalized linear random effects models with varying coefficients. Comput. Stat. Data Anal. 43(1): 13-28 (2003)
1990 – 1999
- 1995
- [j5]Gerhard Tutz, Herbert Groß:
Discrete kernels, loss functions and parametric models in discrete discrimination: A comparative study. Math. Methods Oper. Res. 42(2): 217-230 (1995) - 1990
- [j4]Bernd Morawitz, Gerhard Tutz:
Alternative parameterizations in business tendency surveys. ZOR Methods Model. Oper. Res. 34(2): 143-156 (1990) - [j3]Gerhard Tutz:
Log-linear parameterizations in discriminant analysis. ZOR Methods Model. Oper. Res. 34(4): 303-319 (1990)
1980 – 1989
- 1985
- [j2]Helmut Wegmann, R. Horst, J. Falkinger, Gerhard E. Tutz, Hans Laux, Heinz W. Engl, Anatol Rapoport:
Book reviews. Z. Oper. Research 29(4) (1985) - [j1]Gerhard E. Tutz:
Smoothed additive estimators for non-error rates in multiple discriminant analysis. Pattern Recognit. 18(2): 151-159 (1985)
Coauthor Index
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