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Publications
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. TUTZ, G. (1983): Klassifikation mit kategorialen Merkmalen. Dissertation. Universität Regensburg. OELKER,
M-R., GERTHEISS, J., TUTZ, G. (2012):
Regularization and Model Selection with Categorical Predictors and
Effect Modifiers in Generalized Linear Models. Technical Report 123,
Department of Statistics LMU. TUTZ,
G.,
SCHAUBERGER, G. (2012): Visualization of Categorical
Response Models - from Data Glyphs to Parameters Glyphs. Technical Report 117,
Department of Statistics LMU. HEINZL,
F., TUTZ, G. (2011): Clustering in linear mixed models
with Dirichlet process mixtures using EM algorithm. Technical Report 115, Department of Statistics LMU. PETRY,
S.,
TUTZ, G. (2011): The OSCAR for Generalized Linear
Models. Technical
Report 112, Department of Statistics LMU. GROLL,
A.,
TUTZ, G. (2011): Variable Selection for Generalized
Linear Mixed Models by L1-Penalized
Estimation.
Technical
Report 108, Department of Statistics LMU. PETRY,
S.,
FLEXEDER, C., TUTZ, G. (2011):
Pairwise Fused Lasso. Technical
Report
102, Department of Statistics LMU. ZAHID,
F.,
TUTZ, G. (2011): Proportional Odds Models with
High-dimensional Data Structure. Technical
Report 100, 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. ZAHID, F. M., TUTZ, G. (2010): Multinomial Logit Models with Implicit Variable Selection. Technical Report 89, Department of Statistics LMU. TUTZ, G., GROLL,
A. (2012): Likelihood-Based Boosting in Binary and Ordinal Random
Effects Models. Journal of
Computational and Graphical Statistics, to appear. 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, to
appear. ZAHID, F. M., TUTZ,
G.
(2012): Ridge
Estimation
for
Multinomial
Logit
Models
with
Symmetric
Side
Constraints. Computational Statistics, conditionally accepted. GROLL, A., TUTZ,
G.
(2012):
Regularization
for
Generalized
Additive
Mixed
Models
by
Likelihood-Based
Boosting.
Methods
of Information in Medicine, 51,
168-177. GERTHEISS, J., STELZ,
V.,
TUTZ, G. (2011): Regularization and Model Selection
with Categorical Covariates. GfKl
Proceedings, to appear. ROBINZONOV, N., TUTZ, G., HOTHORN,
T.
(2011):
Boosting
Techniques
for
Nonlinear
Time
Series
Models.
AStA Advances in Statistical Analysis,
to
appear. TUTZ, G., PETRY,
S. (2011): 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. (2010): 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 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 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 and Data 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, 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, 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, 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,
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.
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). 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. |