Publications 

 

BOOKS

TUTZ, G., SCHMID, M. (2016): Modeling Discrete Time-to-Event Data. Springer Series in Statistics.

TUTZ, G. (2012): Regression for Categorical Data. Cambridge University Press.

TUTZ, G. (2000): Die Analyse kategorialer Daten - eine anwendungsorientierte Einführung in Logit-Modellierung und kategoriale Regression. Oldenbourg-Verlag.

FAHRMEIR, L., PIGEOT, I., KÜNSTLER, R., TUTZ, G. (1997, 2009, 7. Auflage): Statistik - der Weg zur Datenanalyse. Springer-Verlag.

FAHRMEIR, L., KÜNSTLER, R., PIGEOT, I., TUTZ, G.,CAPUTO A., LANG, S. (2004, 4. Auflage): Statistik-Aufgabenbuch. Springer-Verlag.

CAPUTO A., FAHRMEIR, L., KÜNSTLER, R., LANG, S., PIGEOT-KÜBLER, I., TUTZ, G.  (2008, 5. Auflage): Statistik-Aufgabenbuch. Springer-Verlag.

FAHRMEIR, L., HAMERLE, A., TUTZ, G. (1996): Multivariate statistische Verfahren. DeGruyter.

FAHRMEIR, L., TUTZ, G. (1994, 2001): Multivariate statistical modelling based on generalized linear models. Springer Series in Statistics.

TUTZ, G. (1990): Modelle für kategoriale Daten mit ordinalem Skalenniveau - parametrische und nonparametrische Ansätze. Vandenhoeck & Ruprecht-Verlag.

HAMERLE, A., TUTZ, G. (1989): Diskrete Modelle zur Analyse von Verweildauern und Lebenszeiten. Campus Verlag.

TUTZ, G. (1989): Latent Trait Modelle für ordinale Beobachtungen - Die statistische und messtheoretische Analyse von Paarvergleichsdaten. Springer-Verlag.



PREPRINTS

TUTZ, G., BERGER, M. (2016): Separating Location and Dispersion in Ordinal Regression Models. Technical Report 186, Department of Statistics LMU.

PÖßNECKER, W., TUTZ, G. (2016): A General Framework for the Selection of Effect Type in Ordinal Regression. Technical Report 186, Department of Statistics LMU.

BERGER, M., TUTZ, G. (2015): Tree-Structured Clustering in Fixed Effects Models, http://arxiv.org/abs/1512.05169

SCHAUBERGER, G., TUTZ, G. (2015): Modelling Heterogeneity in Paired Comparison Data - an L1 Penalty Approach with an Application to Party Preference Data. Technical Report 183, Department of Statistics LMU.

JANITZA, S., TUTZ, G. (2014): Prediction Models for Time Discrete Competing Risks. Technical Report 177, Department of Statistics LMU.

TUTZ, G., SCHNEIDER, M., IANNARIO, M., PICCOLO, D. (2014): Mixture Models for Ordinal Responses to Account for Uncertainty of Choice. Technical Report 175, Department of Statistics LMU.

TUTZ, G., BERGER, M. (2014): Tree-Structured Modelling of Categorical Predictors in Regression. Technical Report 169, Department of Statistics LMU.

TUTZ, G., GROLL, A. (2014): Variable Selection in Discrete Survival Models Including Heterogeneity. Technical Report 167, Department of Statistics LMU.

TUTZ, G., OELKER, M. (2014):  Modeling Clustered Heterogeneity: Fixed Effects, Random Effects and Mixtures. Technical Report 156, Department of Statistics LMU.

HESS, W., TUTZ, G., GERTHEISS, J. (2014): A Flexible Link Function for Discrete-Time Duration Models. Technical Report 155, Department of Statistics LMU.

TUTZ, G., PETRY, S. (2013): Generalized Additive Models with Unknown Link Function Including Variable Selection. Technical Report 145, Department of Statistics LMU.

PETRY, S., TUTZ, G. (2011): The OSCAR for Generalized Linear Models. Technical Report 112, Department of Statistics LMU.

PETRY, S., FLEXEDER, C., TUTZ, G. (2011): Pairwise Fused Lasso. Technical Report 102, Department of Statistics LMU.

ULBRICHT, J., TUTZ, G. (2011): Combining Quadratic Penalization and Variable Selection via Forward Boosting. Technical Report 99, Department of Statistics LMU.


ARTICLES (Selection)

TUTZ, G., BERGER, M. (2016): Response Styles in Rating Scales - Simultaneous Modelling of Content-Related Effects and the Tendency to Middle or Extreme Categories. Journal of Educational and Behavioral Statistics, to appear.

TUTZ, G., GERTHEISS, J. (2016): Regularized Regression for Categorical Data. Statistical Modelling, to appear.

SCHAUBERGER, G., TUTZ, G. (2016): Detection of Differential Item Functioning in Rasch Models by Boosting Techniques. British Journal of Mathematical and Statistical Psychology, 69(1), 80-103.

TUTZ, G., BERGER, M. (2015): Item focussed Trees for the Identification of Items in Differential Item Functioning. Psychometrika, to appear.

JANITZA, S., TUTZ, G., BOULESTEIX, A.-L. (2015): Random Forests for Ordinal Responses: Prediction and Variable selection. Computational Statistics & Data Analysis, to appear.

TUTZ, G., KOCH, D. (2015): Improved Nearest Neighbor Classifiers by Weighting and Selection of Predictors, Statistics and Computing, to appear.

SCHMID, M., KÜCHENHOFF, H., HÖRAUF, A., TUTZ, G. (2015): A survival tree method for the analysis of discrete event times in clinical epidemiological studies. Statistics in Medicine, to appear.

MAUERER, I., PÖßNECKER, W., THURNER, P., TUTZ, G. (2015): Modeling electoral choices in multiparty systems with high-dimensional data: A regularized selection of parameters using the Lasso approach. Journal of Choice Modelling, 16, 23-42.

MÖST, S., PÖßNECKER, W., TUTZ, G. (2015): Variable Selection for Discrete Competing Risks Models. Quality & Quantity, to appear.

TUTZ, G., RAMZAN, S. (2015): Improved Methods for the Imputation of Missing Data by Nearest Neighbor Methods. Computational Statistics & Data Analysis, 90, 84-99.

FUCHS, K., GERTHEISS, J., TUTZ, G., (2015): Nearest Neighbor Ensembles for Functional Data with Interpretable Feature Selection. Chemometrics and Intelligent Laboratory Systems, 146, 186-197.

GROLL, A., SCHAUBERGER, G., TUTZ, G. (2015): Prediction of major international soccer tournaments based on team-specific regularized Poisson regression: An application to the FIFA World Cup 2014. Journal of Quantitative Analysis of Sports, to appear.

OELKER, M., TUTZ, G. (2015): A Uniform Framework for the Combination of Penalties in Generalized Structured Models. Advances in Data Analysis and Classification, published online.

CASALICCHIO, G., TUTZ, G., SCHAUBERGER, G. (2015): Subject-specific Bradley-Terry-Luce Models with Implicit Variable Selection. Statistical Modelling, to appear.

TUTZ, G., PÖßNECKER, W., UHLMANN, L. (2015): Variable Selection in General Multinomial Logit Models. Computational Statistics & Data Analysis, 82, 207-222.

TUTZ, G., SCHAUBERGER, G. (2015): Extended Ordered Pair Comparison Models with Applications to Football Data from German Bundesliga. Advances in Statistical Analysis, 99, 209-227.

OELKER, M.-R., PÖßNECKER, W., TUTZ, G. (2015): Selection and Fusion of Categorical Predictors with L0-Type Penalties. Statistical Modelling, 15, 389-410.

TUTZ, G. (2015): Sequential Models for Ordered Responses. In: W. Van der Linden, R. Hambleton, Handbook of Item Response Theory: Models, Statistical Tools and Applications, Taylor & Francis, to appear.

TUTZ, G., SCHAUBERGER, G. (2015): A Penalty Approach to Differential Item Functioning in Rasch Models. Psychometrika, 80, 21-43.

HEINZL, F., TUTZ, G. (2014): Clustering in Additive Mixed Models with Approximate Dirichlet Process Mixtures: the EM Approach. Statistics and Computing, published online.

OELKER, M.-R., GERTHEISS, J., TUTZ, G. (2014): Regularization and Model Selection with Categorical Predictors and Effect Modifiers in Generalized Linear Models. Statistical Modelling, 14, 157-177.

TUTZ, G., GERTHEISS, J. (2014): Rating Scales as Predictors - the Old Question of Scale Level and some Answers, Psychometrika, published online.

GROLL, A., TUTZ, G. (2014): Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation. Statistics and Computing, 24, 137-154.

HEINZL, F., TUTZ, G. (2014): Clustering in linear mixed models with a group fused lasso penalty. Biometrical Journal, 1, 44-68.

SCHAUBERGER, G. TUTZ, G. (2014): Regularization Methods in Economic Forecasting. In: J. Beran, Y. Feng, H. Hebbel, Empirical Economic and Financial Research - Theory, Methods and Practice, Advanced Studies in Theoretical and Applied Econometrics, Vol. 48, Springer.

BÜHLMANN, P., GERTHEISS, J., HIEKE, S., KNEIB, T., MA, S., SCHUMACHER, M., TUTZ, G., WANG, C.-Y., WANG, Z., ZIEGLER, A.  (2014): Discussion of The Evolution of Boosting Algorithms and Extending Statistical Boosting. Methods of Information in Medicine, 53, 436-445.

DRAXLER, C., TUTZ, G. (2014): Comparison of maximum likelihood with conditional composite likelihood estimation of person parameters in the Rasch model. Communications in Statistics - Simulation and Computation, to appear.

ZAHID, F.M., TUTZ, G. (2013): Proportional Odds Models with High-dimensional Data Structure. International Statistical Review, 81, 388-406.

ZAHID, F. M., TUTZ, G. (2013): Multinomial Logit Models with Implicit Variable Selection. Advances in Data Analysis and Classification, 7, 393-416.

HEINZL, F., TUTZ, G. (2013): Clustering in Linear Mixed Models with Dirichlet Process Mixtures using EM Algorithm, Statistical Modelling, 13, 41-67.

TUTZ, G., SCHAUBERGER, G. (2013): Visualization of Categorical Response Models - from Data Glyphs to Parameter Glyphs. Journal of Computational and Graphical Statistics, 22, 156-177.

ZAHID, F. M., TUTZ, G. (2013): Ridge Estimation for Multinomial Logit Models with Symmetric Side Constraints. Computational Statistics, 28, 1017-1034.

TUTZ, G., GROLL, A. (2013): Likelihood-Based Boosting in Binary and Ordinal Random Effects Models. Journal of Computational and Graphical Statistics, 22, 356-378.

GERTHEISS, J., STELZ, V., TUTZ, G. (2013): Regularization and Model Selection with Categorical Covariates. In: B. Lausen, D. Van den Poel, A. Ultsch, Algorithms from and for Nature and Life, 215-222, Springer.

PETRY, S., TUTZ, G. (2012): Shrinkage and Variable Selection by  Polytopes. Journal of Statistical Planning and Inference, 142, 48-64.

GERTHEISS, J., TUTZ, G. (2012): Regularization and Model Selection with Categorical Effects Modifiers. Statistica Sinica, 22, 957-982.

GROLL, A., TUTZ, G. (2012): Regularization for Generalized Additive Mixed Models by Likelihood-Based Boosting. Methods of Information in Medicine, 51, 168-177.

OELKER, M-R., GERTHEISS, J., TUTZ, G. (2012): Categorical Effect Modifiers in Generalized Linear Models, Proceedings of COMPSTAT 2012.

ROBINZONOV, N., TUTZ, G., HOTHORN, T. (2012): Boosting Techniques for Nonlinear Time Series Models. AStA Advances in Statistical Analysis 96, 99-122.

TUTZ, G., PETRY, S. (2012): Nonparametric Estimation of the Link Function Including Variable Selection. Statistics and Computing, 21, 545-561.

LEITENSTORFER, F., TUTZ, G. (2011): Estimation of Single-Index Models Based on Boosting Techniques. Statistical Modelling, 11, 203-217.

GERTHEISS, J., HOGGER, S., OBERHAUSER, C., TUTZ, G. (2011): Selection of Ordinally Scaled Independent Variables with Applications to International Classification of Functioning Core Sets. Journal of the Royal Statistical Society: Series C, 60, 377-396.

TUTZ, G. (2011): Poisson Regression. In: M. Lovric, International Encyclopedia of Statistical Sciences, 1075-1077, Springer.

GERTHEISS, J., TUTZ, G. (2010): Sparse Modeling of Categorial Explanatory Variables. The Annals of Applied Statistics, 4, 2150-2180.

SLAWSKI, M., zu CASTELL, W., TUTZ, G. (2010): Feature Extraction Guided by Structural Information. The Annals of Applied Statistics, 4, 1056-1080.

TUTZ, G., GERTHEISS, J. (2010): Feature Extraction in Signal Regression: A Boosting Technique for Functional Data Regression. Journal of Computational and Graphical Statistics, 19, 154-174.

TUTZ, G. (2010): Editorial: Regularisation Methods in Regression and Classification. Statistics and Computing, 20, 117-118.

TUTZ, G. (2010): Regression für Zählvariablen. In: H. Best, C. Wolf, Handbuch der sozialwissenschaftlichen Datenanalyse, Vahlen Verlag, 859-876.

TUTZ, G., GROLL, A. (2010): Generalized Linear Mixed Models Based on Boosting. In: T. Kneib, G. Tutz, Statistical Modelling and Regression Structures - Festschrift in Honour of Ludwig Fahrmeir, Physica.

TUTZ, G., STROBL, C. (2010): Generalisierte lineare Modelle. In: H. Holling, B. Schmitz, Handbuch der psychologischen Methoden und Evaluation, Hofgrefe Verlag, 509-517.

SPIESS, M., TUTZ, G. (2010): Logistische Regressionsverfahren für mehrkategoriale Zielvariablen. In: B. Schmitz, H. Holling, Handbuch der psychologischen Methoden und
Evaluation, Hogrefe Verlag, 509-517.

GERTHEISS, J., TUTZ, G. (2009): Feature Selection and Weighting by Nearest Neighbor Ensembles. Chemometrics and Intelligent Laboratory Systems, 99, 30-38.

GERTHEISS, J., TUTZ, G. (2009): Penalized Regression with Ordinal Predictors. International Statistical Review, 77, 354-365.

GERTHEISS, J., TUTZ, G. (2009): Variable Scaling and Nearest Neighbor Methods, Chemometrics, 23,  149-151.

GERTHEISS, J., TUTZ, G. (2009): Supervised Feature Selection in Mass Spectrometry
based Proteomic Profiling by Blockwise Boosting, Bioinformatics, 8, 1076-1077.

STROBL, C., MALLEY, J., TUTZ, G. (2009): An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests. Psychological Methods, 14, 323-348.

KNEIB, T., HOTHORN, T., TUTZ, G. (2009): Variable Selection and Model Choice in Geoadditive Regression Models. Biometrics, 65, 626-634.

TUTZ, G., ULBRICHT, J. (2009): Penalized Regression with Correlation Based Penalty, Statistics and Computing, 19, 239-253.

SHAFIK, N., TUTZ, G. (2009): Boosting Nonlinear Additive Autoregressive Time Series, Computational Statistics andData Analysis, 53, 2453-2464.

GERTHEISS, J., Tutz, G. (2009): Statistische Tests. In: M. Schwaiger, A. Meyer, Theorien und Methoden der Betriebswirtschaft, Vahlen Verlag, 439-454.

KRAEMER, N., BOULESTEIX, A., TUTZ, G. (2008): Penalized Partial Least Squares Based on B-Splines. Chemometrics and Intelligent Laboratory Systems, 94, 60-69.

BINDER, H., TUTZ, G. (2008): Fitting Generalized Additive Models: A Comparison of Methods. Statistics and Computing, 18, 87-99.

REITHINGER, F., JANK, W., TUTZ, G., SHMUELI, G. (2008): Smoothing Sparse and Unevenly Sampled Curves Using Semiparametric Mixed Models: An Application to Online Auctions. JRSS Series C: Applied Statistics, 57, 127-148.

VAN DER LINDE, A., TUTZ, G. (2008): On association in regression: the coefficient of determination revisited. Statistics, 42, 1-24.

ULBRICHT, J. TUTZ, G. (2008): Boosting Correlation Based Penalization in Generalized Linear Models. In: Shalabh and C. Heumann, Recent Advances In Linear Models and Related Areas. Springer, 165-180.

TUTZ, G., BINDER, H. (2007): Boosting Ridge Regression. Computational Statistics & Data Analysis, 51, 6044-6059.

TUTZ, G., REITHINGER, F. (2007): Flexible semiparametric mixed models. Statistics in Medicine, 26, 2872-2900.

LEITENSTORFER, F., TUTZ, G. (2007): Generalized Monotonic Regression Based on B-Splines with an Application to Air Pollution Data. Biostatistics, 8, 654-673.

LEITENSTORFER, F., TUTZ, G. (2007): Knot Selection by Boosting Techniques, Computational Statistics & Data Analysis, 51, 4605-4621.

LEITENSTORFER, F., TUTZ, G. (2007): A Boosting Approach to Generalized Monotonic Regression. In R. Decker, H.-J. Lenz (Eds.), Advances in Data Analysis, Proceedings of the 30th Annual Conference of the Gesellschaft für Klassifikation, pp. 245-254, Berlin: Springer

TUTZ, G., LEITENSTORFER, F. (2007): Generalized smooth monotonic regression in additive modelling. Journal of Computational and Graphical Statistics, 16, 165-188.

LEITENSTORFER, F., TUTZ, G. (2006): A Boosting Approach to Generalized Monotonic Regression. In: R. Decker, H.-J. Lenz (eds.), Advances in Data Analysis, 245-254, Berlin: Springer.

TUTZ, G. (2006): Categorical Response Models. In: Encyclopedia of Clinical Trials (to appear).

TUTZ, G. (2006): Models for polytomous data. In: P. Armitage, T. Colton (eds.), Encyclopedia of Biostatistics, second edition, Wiley.

EINBECK, J., TUTZ, G. (2006): Modelling beyond Regression Functions: an Application of Multimodal Regression to Speed-Flow Data. Applied Statistics 55, 461-475.

TUTZ, G., BINDER, H. (2006): Generalized additive modelling with implicit variable selection by likelihood based boosting. Biometrics 62, 961-971.

TUTZ, G., LEITENSTORFER, F. (2006): Response shrinkage estimators in binary regression. Computational Statistics & Data Analysis 50, 2878-2901.

BOULSTEIX, A. L., TUTZ, G. (2006): Identification of Interaction Patterns and Classification with Applications to Microarray Data. Computational Statistics & Data Analysis 50, 783-802.

KRAUSE, R., TUTZ, G. (2006): Genetic Algorithms for the Selection of Smoothing Parameters in Additive Models. Computational Statistics 21, 8-31.

TUTZ, G., ULBRICHT, J. (2006): An Alternative Approach to Regularization and Variable Selection in High Dimensional Regression Modelling. In: J. Hinde, J. Einbeck, J. Newell (eds.) Proceedings of the 21st International Workshop on Statistical Modelling, 486-493.

EINBECK, J., TUTZ, G. (2006): The fitting of multifunctions: an approach to nonparametric multimodal regression. In A. Rizzi, M. Vichi (eds.), COMPSTAT 2006, Proceedings in Computational Statistics, 1243-1250, Heidelberg: Physica.

LEITENSTORFER, F., TUTZ, G. (2006): Smoothing with Curvature Constraints based on Boosting Techniques. In A. Rizzi, M. Vichi (eds.), COMPSTAT 2006, Proceedings in Computational Statistics, 1267-1276, Heidelberg: Physica.

TUTZ, G. (2005): Modelling of repeated ordered measurements by isotonic sequential regression. Statistical Modelling 5, 269-287.

TUTZ, G., BINDER, H. (2005): Localized Classification. Statistics and Computing 15, 155-166.

TUTZ, G., HECHENBICHLER, K. (2005): Aggregating Classifiers With Ordinal Response Structure. Journal of Statistical Computation and Simulation 75, 391-408.

EINBECK, J., TUTZ, G., EVERS, L. (2005): Local principal curves. Statistics and Computing 15, 301-313.

KAUERMANN, G., TUTZ, G., BRÜDERL, J. (2005): The Survival of Newly Founded Companies. Journal of the Royal Statistical Society A 168, 145-158

EINBECK, J., TUTZ, G., EVERS, L. (2005): Exploring Multivariate Data Structures with Local Principal Curves. In: C. Weihs, W. Gaul, Classification – the Ubiquitous Challenge, 256-265.

HECHENBICHLER, K., TUTZ, G. (2005): Bagging, boosting and Ordinal Classification. In: C. Weihs, W. Gaul, Classification – the Ubiquitous Challenge, 145-152.

BINDER, H., TUTZ, G. (2004): Localized logistic classification with variable selection. In: J. Antoch (Ed.) COMPSTAT 2004, Physica Verlag.

SPIESS, M., TUTZ, G. (2004): Alternative measures of the explanatory power of multivariate pro-bit models with continuous or ordinal responses. Journal of Mathematical Sociology 28, 125-146.

TUTZ, G., BINDER, H. (2004): Flexible modelling of discrete failure time including time-varying smooth effects. Statistics in Medicine 23, 2445-2461.

TUTZ, G., SCHOLZ, T. (2004): Semiparametric modelling of multicategorical data. Journal of Statistical Computation and Simulation 74, 183-200.

BOULESTEIX, A., TUTZ, G. STRIMMER, K. (2003): A CART-based Approach to Discover Emerging Patterns in Microarray Data, Bioinformatics 19, 1-8.

KAUERMANN, G., TUTZ, G. (2003): Semiparametric Modelling of Ordinal Data. Journal of Computational and Graphical analysis 12, 176-196.

KRAUSE, R., TUTZ, G. (2003): Simultaneous selection of variables and smoothing parameter in additive models. In: D. Baier, K.-D. Wernecke, Innovations in Classification, Data Analysis, and Information Systems, 146-153.

TUTZ, G. (2003): Generalized semiparametrically structured mixed models. Computational Statistics and Data Analysis 46, 777-800.

TUTZ, G. (2003): Generalized semiparametrically structured ordinal models. Biometrics 59, 263-273.

TUTZ, G., KAUERMANN, G. (2003): Generalized linear random effects models with varying coefficients. Computational Statistics & Data Analysis 43, 13-28.

DREESMAN, J., TUTZ, G. (2001): Nonstationary conditional models for spatial data based on varying coefficients. Journal of the Royal Statistical Society D 50, 1-15.

KAUERMANN, G., TUTZ, G. (2001): Testing generalized linear and semiparametric models against smooth alternatives. Journal of the Royal Statistical Society B 63, 147-166.

KAUERMANN, G., TUTZ, G. (2000): Local likelihood estimation and bias reduction in varying coefficient models. Journal of Nonparametric Statistics 12, 343-371.

KAUERMANN, G., TUTZ, G. (1999): On model diagnostics and bootstrapping in varying coefficient models. Biometrika 86, 119-128.

SIMONOFF, J., TUTZ, G. (1999): Smoothing methods for discrete data. In: M. Schimek (Hrsg): Smoothing and Regression. Approaches, Computation and Application, Wiley.

EDLICH, S., KAUERMANN, G., TUTZ, G. (1998): Smoothing ordinal data by semiparametric models. Proceedings of the 13th International Workshop on Statistical Modelling. New Orleans.

TUTZ, G., KAUERMANN, G. (1998): Locally weighted least squares in categorical varying-coefficient models. In: R. Galata, H. Küchenhoff (eds.) Econometrics in Theory & Practice, Festschrift für Hans Schneeweiß (p. 119-130).

TUTZ, G. (1998): Time-Varying coefficients for discrete panel data with an application to business tendency surveys. Jahrbücher für Nationalökonomie und Statistik 217, 334-344.

KAUERMANN, G., TUTZ, G. (1997): Local estimators in multivariate generalized linear models with varying coefficients. Computational Statistics 12, 193-208.

KAUERMANN, G., TUTZ, G. (1997): Testing generalized linear models against smooth alternatives. Schriftenreihe der östereichischen Statistischen Gesellschaft Band 5, 190-194.

TUTZ, G. (1997): Models for polytomous data. In: A. Agresti (ed.) Categorical Data Analysis. Encyclopedia of Biostatistics, Wiley.

TUTZ, G. (1997): Sequential Models for Ordered Responses. In: W. Van der Linden, R. Hambleton (Eds.), Handbook of Item Response Theory (p. 139-152).

TUTZ, G., PRITSCHER, L. (1996): Nonparametric estimation of discrete hazard functions. Lifetime Data Analysis 2, 291-308.

TUTZ, G., HENNEVOGL, W. (1996): Random effects in ordinal regression models. Computational Statistics and Data Analysis 22, 537- 557.

TUTZ, G. (1995): Competing risks models in discrete time with nominal or ordinal categories of response. Quality & Quantity 29, 405-420.

TUTZ, G., GROSS, H. (1995): Discrete kernels, parametric models and loss functions in discrete discrimination -- a comparative study. ZOR-- Methods and Models in Operations Research 42, 217-230.

TUTZ, G. (1995): Smoothing for categorical data: Discrete kernel regression and local likelihood approaches. In: H. H. Bock, W. Polasek (Eds.), Data Analysis and Information Systems 261-271, Springer-Verlag.

FAHRMEIR, L., TUTZ, G. (1994): Dynamic stochastic models for time-dependent ordered paired comparison systems. Journal of the American Statistical Association 89, 1438-1449.

TUTZ, G. (1993): Invariance principles and scale information in regression models. Methodika VII, 112-119.

TUTZ, G. (1993): Regressionsanalyse mit einer ordinalen abhängigen Variable -- Modellierungsansätze im Rahmen verallgemeinerter lineare Modelle und Schätzungen im GLAMOUR. Allgemeines Statistisches Archiv 77, 183-204.

TUTZ, G. (1992): Discrete survival time models using GLAMOUR. Biometrie und Informatik in Medizin und Biologie 23, 167-184.

TUTZ, G. (1992): Graphische Methoden für kategorial-ordinale Daten. In: H. Enke, H. J. Gölles, H. R. Haux, H. K.-D. Wernecke (Eds.), Methoden und Werkzeuge für die exploratorische Datenanalyse. Fischer Verlag.

TUTZ, G. (1991): Sequential models in ordinal regression. Computational Statistics & Data Analysis 11, 275-295.

GEORG, H., TUTZ, G. (1991): Diskrete Hazardraten-Modelle in der Shell-Jugendstudie. Zentralarchiv für empirische Sozialforschung 29, 81-93.

TUTZ, G. (1991): Choice of smoothing parameters for direct kernels in discrimination. Biometrical Journal 33, 519-527.

TUTZ, G. (1991): Consistency of cross-validatory choice of smoothing parameters for direct kernel estimates. Computational Statistics Quarterly 4, 295-314.

TUTZ, G. (1990): Smoothed categorical regression based on direct kernel estimates. Journal of Statistical Computation and Simulation 36, 139-156.

TUTZ, G. (1990): Log-linear parameterization in discrete discriminant analysis. ZOR -- Methods and Models of Operations Research 34, 303-319.

TUTZ, G., MORAWITZ, B. (1990): Parameterizations for business survey data. ZOR -- Methods and Models of Operations Research 34, 143-156.

TUTZ, G. (1990): Sequential item response models with an ordered response. British Journal of Statistical and Mathematical Psychology 43, 39-55.

TUTZ, G. (1989): On cross-validation for discrete kernel estimates in discrimination. Communications in Statistics, Theory and Methods 11, 4145-4162.

TUTZ, G. (1989): Compound regression models for categorical ordinal data. Biometrical Journal 31, 259-272.

TUTZ, G. (1988): Sufficiency of variables in discrete discriminant analysis. Statistical Papers/Statistische Hefte 29, 257 - 269.

TUTZ, G. (1988): Smoothing for discrete kernels in discrimination. Biometrical Journal 6, 729-739.

TUTZ, G. (1986): An alternative choice of smoothing for kernel-based density estimates in discrete discriminant analysis. Biometrika 73, 405-4116.

TUTZ, G. (1986): Bradley-Terry-Luce models with an ordered response. Journal of Mathematical Psychology 30, 306-316.

TUTZ, G. (1985): Diskrete probabilistische Reaktionsmodelle als kategoriale Regressionsansätze. Archiv für Psychologie 2, 99-114.

TUTZ, G. (1984): Verzerrungskorrektur bei additiven Schätzern der Trefferrate. In: H. H. Bock (Ed.), Studien zur Klassifikation, Band 15, (pp 122-131). Frankfurt: Indeks Verlag.

TUTZ, G. (1984): Smoothed additive estimators for nonerror rates in multiple discriminant analysis. Pattern Recognition 18, 151-159.

FAHRMEIR, L., HAMERLE, A., TUTZ, G. (1982): Zur Modellwahl und Variablenselektion bei nichtmetrischen Klassifikationsproblemen. In: Ihm, J. Dahlberg (Eds.) Studien zur Klassifikation, Band 10. Frankfurt: Indeks Verlag.

HAMERLE, A., TUTZ, G. (1980): Goodness of fit tests for probabilistic measurement models. Journal of Mathematical Psychology 21, 153-167.

HAMERLE, A., TUTZ, G. (1980): Zur experimentellen Validierung von probabilistischen verbundenen Meßstrukturen. Zeitschrift für experimentelle und angewandte Psychologie 27, 213-230.

HAMERLE, A., TUTZ, G. (1980): Kategoriale Reaktionen in multifaktoriellen Versuchsplänen und mehrdimensionale Zusammenhangsanalysen. Archiv für Psychologie 133, 53-58.