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Prof. Dr. Gerhard Tutz
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AdresseInstitut für StatistikSeminar für angewandte Stochastik Ludwig-Maximilians-Universität Akademiestraße 1, Zimmer 457 D-80799 München |
KontaktTel: (+49 89) 2180 3044Fax: (+49 89) 2180 5308 E-mail: gerhard.tutz[at] stat.uni-muenchen.de Sprechstunde: Di 14.00-15.00 Uhr SekretariatBarbara NishnikTel: (+49 89) 2180 2814 Fax: (+49 89) 2180 5308 Öffnungszeiten: Werktags 8.30-12.00 Uhr und 14.00-17.00 Uhr |
| 19.09.2009 | Es ist keine Anmeldung zum Seminar "Klassische und neuere Ansätze zur Analyse kategorialer Daten" mehr möglich. |
| Analyse kategorialer Daten | Analysis of categorical data |
| Feature Extraction | Feature Extraction |
| Regularisierung und strukturierte Regression | Regularization and Structured Regression |
| Gemischte Modelle | Mixed Models |
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Feature extraction aims at detecting influential
structures or patterns in data. The focus of the project is on model
based feature selection methods where the predictor space is linked to
the target criterion by parametric or semiparametric models and
features are extracted with reference to the modelling approaches. The
supervised learning techniques that are considered explicitly use the
target criterion in the feature selection process in contrast to widely
used two-step approaches where in the first step unsupervised learning
is applied to extract features and only in the second step the features
are linked to the target.
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Publications
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, 2007, 6. 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.
GERTHEISS, J.,
HOGGER, S., OBERHAUSER, C., TUTZ, G. (2009): Selection
of Ordinally Scaled Independent Variables. Technical Report 62, Department of Statistics LMU.
GERTHEISS, J., TUTZ, G. (2009): Sparse
Modeling of Categorial Explanatory Variables. Technical Report 60, Department of Statistics LMU.
PETRY, S., TUTZ, G. (2009): Shrinkage and
Variable Selection by Polytopes. Technical
Report 53, Department of
Statistics LMU.
KRAUSE,R., TUTZ,
G. (2004): Variable selection and siscrimination in gene
expression data by genetic algorithms. SFB
Discussion Paper 390.
TUTZ, G., SCHOLZ,
T. (2003): Ordinal regression modelling between proportional and non
proportinal odds. SFB
Discussion Paper 304.
LEITENSTORFER, F., TUTZ, G. (2009):
Estimation of Single-Index Models Based on Boosting Techniques. Statistical
Modelling, to appear.
GERTHEISS, J., TUTZ, G. (2009):
Feature Selection and Weighting by Nearest Neighbor Ensembles. Chemometrics and Intelligent Laboratory
Systems, to appear
GERTHEISS, J., TUTZ, G. (2009): Penalized Regression with Ordinal Predictors. International Statistical Review, to appear.
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.
TUTZ, G., GERTHEISS, J. (2009): Feature
Extraction in Signal Regression: A Boosting Technique for Functional
Data Regression, Journal of
Computational and Graphical
Statistics, to appear.
KNEIB, T., HOTHORN, T., AND
TUTZ, G. (2009):
Variable Selection and
Model Choice in Geoadditive Regression
Models. Biometrics, 6, 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.
TUTZ, G., STROBL, C. (2009):
Generalisierte lineare Modelle. In: B. Schmitz, H. Holling, Handbuch
der psychologischen Methoden und Evaluation, Hogrefe Verlag (to appear).
SPIESS, M., TUTZ, G. (2009): Logistische
Regressionsverfahren für mehrkategoriale Zielvariablen. In: B.
Schmitz, H. Holling, Handbuch der psychologischen Methoden und
Evaluation, Hogrefe Verlag (to appear).
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, 2, 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 (to appear).
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.