Ridge Estimation for Multinomial Logit Models with Symmetric Side Constraints (2009)
Zahid, Faisal Maqbool, Tutz, Gerhard
In multinomial logit models, the identifiability of parameter estimates is typically obtained by side constraints that specify one of the response categories as reference category. When parameters...
Selection of Ordinally Scaled Independent Variables (2009)
Gertheiss, Jan, Hogger, Sara, Oberhauser, Cornelia, Tutz, Gerhard
Ordinal categorial variables are a common case in regression modeling. Although the case of ordinal response variables has been well investigated, less work has been done concerning ordinal...
Shrinkage and Variable Selection by Polytopes (2009)
Petry, Sebastian, Tutz, Gerhard
Constrained estimators that enforce variable selection and grouping of highly correlated data have been shown to be successful in finding sparse representations and obtaining good performance in...
Strobl, Carolin, Malley, James, Tutz, Gerhard
Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, that can deal with large...
Feature Selection Guided by Structural Information (2009)
Slawski, Martin, Zu Castell, Wolfgang, Tutz, Gerhard
In generalized linear regression problems with an abundant number of features, lasso-type regularization which imposes an l1-constraint on the regression coefficients has become a widely established...
Variable Selection and Model Choice in Geoadditive Regression Models (2009)
Thomas Kneib, Torsten Hothorn, Gerhard Tutz, Thomas Kneib, Torsten Hothorn, Gerhard Tutz
Model choice and variable selection are issues of major concern in practi-cal regression analyses. We propose a boosting procedure that facilitates both tasks in a class of complex geoadditive...
Sparse Modeling of Categorial Explanatory Variables (2009)
Shrinking methods in regression analysis are usually designed for metric predictors. If independent variables are categorial some modifications are necessary. In this article two L1-penalty based...
Summary: When feature selection in mass spectrometry is based on single m/z values, problems arise from the fact that variability is not only in vertical but also in horizontal direction, i.e. also...
Estimation of Single-Index Models Based on Boosting Techniques (2008)
Leitenstorfer, Florian, Tutz, Gerhard
In single-index models the link or response function is not considered as fixed. The data determine the form of the unknown link function. In order to obtain a flexible form of the link function we...
Feature Selection and Weighting by Nearest Neighbor Ensembles (2008)
In the field of statistical discrimination nearest neighbor methods are a well known, quite simple but successful nonparametric classification tool. In higher dimensions, however, predictive power...
Identification of interaction patterns and (2008)
Anne-laure Boulesteix, Gerhard Tutz
www.elsevier.com/locate/csda
Penalized Regression with Ordinal Predictors (2008)
Ordered categorial predictors are a common case in regression modeling. In contrast to the case of ordinal response variables, ordinal predictors have been largely neglected in the literature. In...
Krämer, Nicole, Boulesteix, Anne-Laure, Tutz, Gerhard
We propose a novel framework that combines penalization techniques with Partial Least Squares (PLS). We focus on two important applications. (1) We combine PLS with a roughness penalty to estimate...
Feature Extraction in Signal Regression: A Boosting Technique for Functional Data Regression (2007)
Main objectives of feature extraction in signal regression are the improvement of accuracy of prediction on future data and identification of relevant parts of the signal. A feature extraction...
Boosting Correlation Based Penalization in Generalized Linear Models (2007)
In high dimensional regression problems penalization techniques are a useful tool for estimation and variable selection. We propose a novel penalization technique that aims at the grouping effect...
Nonstationary Conditional Models for Spatial Data Based on Varying Coefficients (2007)
Johannes Dreesman, Gerhard Tutz
: The analysis of spatial data by means of Markov random elds usually is based on strict stationarity assumptions. Although these assumptions rarely hold, they are necessary in order to obtain...
Comparison Between Local Estimates for Multi-Categorical Varying-Coefficent Models (2007)
Gerhard Tutz, Silke Edlich, Christoph Bäumer
this paper is to investigate small sample properties of two types of estimates and compare their performance. The first estimate is based on the local likelihood principle (Tibshirani & Hastie,...
Testing Generalized Linear and Semiparametric Models Against Smooth Alternatives (2007)
We propose goodness of fit tests for testing generalized linear models and semiparametric regression models against smooth alternatives. The focus is on models having both, continuous and factorial...
Generalized monotonic regression based on B-splines with an application to air pollution data (2007)
Leitenstorfer, Florian, Tutz, Gerhard
In many studies, it is known that one or more of the covariates have a monotonic effect on the response variable. In these circumstances, standard fitting methods for generalized additive models...
Krämer, Nicole, Boulesteix, Anne-Laure, Tutz, Gerhard
We propose a novel framework that combines penalization with Partial Least Squares (PLS). Starting with a generalized additive model, we expand each additive component in terms of a generous amount...
Variable Selection and Model Choice in Geoadditive Regression Models (2007)
Kneib, Thomas, Hothorn, Torsten, Tutz, Gerhard
Model choice and variable selection are issues of major concern in practical regression analyses. We propose a boosting procedure that facilitates both tasks in a class of complex geoadditive...
Boosting Nonlinear Additive Autoregressive Time Series (2007)
Within the last years several methods for the analysis of nonlinear autoregressive time series have been proposed. As in linear autoregressive models main problems are model identification,...
Feature Extraction in Signal Regression: A Boosting Technique for Functional Data Regression (2007)
Main objectives of feature extraction in signal regression are the improvement of accuracy of prediction on future data and identification of relevant parts of the signal. A feature extraction...
Knot selection by boosting techniques (2007)
Florian Leitenstorfer, Gerhard Tutz
A novel concept for estimating smooth functions by selection techniques based on boosting is developed. It is suggested to put radial basis functions with different spreads at each knot and to do...
Boosting Nonlinear Additive Autoregressive Time Series (2007)
Nivien Shafik, Gerhard Tutz, Nivien Shafik, Gerhard Tutz
Within the last years several methods for the analysis of nonlinear autoregressive time series have been proposed. As in linear autoregressive models main problems are model identification,...
Knot selection by boosting techniques (2007)
Florian Leitenstorfer, Gerhard Tutz
A novel concept for estimating smooth functions by selection techniques based on boosting is developed. It is suggested to put radial basis functions with different spreads at each knot and to do...
Penalized Partial Least Squares Based on B-Splines Transformations (2006)
Kraemer, Nicole, Boulesteix, Anne-Laure, Tutz, Gerhard
We propose a novel method to model nonlinear regression problems by adapting the principle of penalization to Partial Least Squares (PLS). Starting with a generalized additive model, we expand the...
Smoothing with Curvature Constraints based on Boosting Techniques (2006)
Leitenstorfer, Florian, Tutz, Gerhard
In many applications it is known that the underlying smooth function is constrained to have a specific form. In the present paper, we propose an estimation method based on the regression spline...
Knot selection by boosting techniques (2006)
Leitenstorfer, Florian, Tutz, Gerhard
A novel concept for estimating smooth functions by selection techniques based on boosting is developed. It is suggested to put radial basis functions with different spreads at each knot and to do...
Reithinger, Florian, Jank, Wolfgang, Tutz, Gerhard, Shmueli, Galit
Functional data analysis can be challenging when the functional objects are sampled only very sparsely and unevenly. Most approaches rely on smoothing to recover the underlying functional object from...
Penalized Partial Least Squares Based on B-Splines Transformations (2006)
Krämer, N., Boulesteix, Anne-Laure, Tutz, Gerhard
We propose a novel method to model nonlinear regression problems by adapting the principle of penalization to Partial Least Squares (PLS). Starting with a generalized additive model, we expand the...
Penalized Regression with Correlation Based Penalty (2006)
A new regularization method for regression models is proposed. The criterion to be minimized contains a penalty term which explicitly links strength of penalization to the correlation between...
Florian Reithinger, Wolfgang Jank, Gerhard Tutz, Galit Shmueli
Smoothing sparse and unevenly sampled curves using semiparametric mixed models: An application to online auctions
Smoothing with curvature constraints based on boosting techniques (2006)
Florian Leitenstorfer, Gerhard Tutz
In many applications it is known that the underlying smooth function is constrained to have a specific form. In the present paper, we propose an estimation method based on the regression spline...
Generalized Monotonic Regression Based on B-Splines with an Application to Air Pollution Data (2006)
Leitenstorfer, Florian, Tutz, Gerhard
In many studies it is known that one or more of the covariates have a monotonic effect on the response variable. In these circumstances, standard fitting methods for generalized additive models (GAM)...
Generalized smooth monotonic regression (2005)
Tutz, Gerhard, Leitenstorfer, Florian
Common approaches to monotonic regression focus on the case of a unidimensional covariate and continuous dependent variable. Here a general approach is proposed that allows for additive and...
Boosting Ridge Regression (2005)
Ridge regression is a well established method to shrink regression parameters towards zero, thereby securing existence of estimates. The present paper investigates several approaches to combining...
Generalized Monotonic Regression Based on B-Splines with an Application to Air Pollution Data (2005)
Leitenstorfer, Florian, Tutz, Gerhard
In many studies where it is known that one or more of the certain covariates have monotonic effect on the response variable, common fitting methods for generalized additive models (GAM) may be...
Flexible semiparametric mixed models (2005)
Tutz, Gerhard, Reithinger, Florian
In linear mixed models the influence of covariates is restricted to a strictly parametric form. With the rise of semi- and nonparametric regression also the mixed model has been expanded to allow for...
Boulesteix, Anne-Laure, Tutz, Gerhard
Emerging patterns represent a class of interaction structures which has been recently proposed as a tool in data mining. In this paper, a new and more general definition refering to underlying...
The main problem with localized discriminant techniques is the curse of dimensionality, which seems to restrict their use to the case of few variables. This restriction does not hold if localization...
Simultaneous selection of variables and smoothing parameters by genetic algorithms (2004)
Krause, Rüdiger, Tutz, Gerhard
In additive models the problem of variable selection is strongly linked to the choice of the amount of smoothing used for components that represent metrical variables. Many software packages use...
Variable selection and discrimination in gene expression data by genetic algorithms (2004)
Krause, Rüdiger, Tutz, Gerhard
Gene expression datasets usually have thousends of explanatory variables which are observed on only few samples. Generally most variables of a dataset have no effect and one is interested in...
On association in regression: the coefficient of determination revisited (2004)
Linde, A. Van Der, Tutz, Gerhard
Universal coefficients of determination are investigated which quantify the strength of the relation between a vector of dependent variables Y and a vector of independent covariates X. They are...
Einbeck, Jochen, Tutz, Gerhard
An enormous amount of publications deals with smoothing in the sense of nonparametric regression. However, nearly all of the literature treats the case where predictors and response are related in...
Generalized additive modelling with implicit variable selection by likelihood based boosting (2004)
The use of generalized additive models in statistical data analysis suffers from the restriction to few explanatory variables and the problems of selection of smoothing parameters. Generalized...
Localized Classification (2004)
The main problem with localized discriminant techniques is the curse of dimensionality, which seems to restrict their use to the case of few variables. This restriction does not hold if localization...
Anne-laure Boulesteix, Gerhard Tutz
Emerging patterns represent a class of interaction structures which has been recently proposed as a tool in data mining. In this paper, a new and more general definition refering to underlying...
Anne-laure Boulesteix, Gerhard Tutz
Emerging patterns represent a class of interaction structures which has been recently proposed as a tool in data mining. In this paper, a new and more general definition refering to underlying...
Ordinal regression modelling between proportional odds and non-proportional odds (2003)
The proportional odds model has become the most widely used model in ordinal regression. Despite favourable properties in applications it is often an inappropriate simplification yielding bad data...
Additive Modelling with Penalized Regression Splines and Genetic Algorithms (2003)
Krause, Rüdiger, Tutz, Gerhard
Additive models of the type y=f_1(x_1)+...+f_p(x_p)+e where f_j,j=1,...,p, have unspecified functional form, are flexible statistical regression models which can be used to characterize nonlinear...
A Framework to Discover Emerging Patterns for Application in Microarray Data (2003)
Boulesteix, Anne-Laure, Tutz, Gerhard
Various supervised learning and gene selection methods have been used for cancer diagnosis. Most of these methods do not consider interactions between genes, although this might be interesting...
Response smoothing estimators in binary regression (2003)
A shrinkage type estimator is introduced which has favorable properties in binary regression. Although binary observations are never very far away from the underlying probability, in all interesting...
Einbeck, Jochen, Tutz, Gerhard, Evers, L.
Principal components are a well established tool in dimension reduction. The extension to principal curves allows for general smooth curves which pass through the middle of a p-dimensional data...
Aggregating Classifiers With Ordinal Response Structure (2003)
Tutz, Gerhard, Hechenbichler, K.
In recent years the introduction of aggregation methods led to many new techniques within the field of prediction and classification. The most important developments, bagging and boosting, have been...
A Framework to Discover Emerging Patterns for Application in Microarray Data (2003)
Anne-laure Boulesteix, Gerhard Tutz
supervised learning Various supervised learning and gene selection methods have been used for cancer diagnosis. Most of these methods do not consider interactions between genes, although this might...
A CART-based approach to discover emerging patterns in microarray data (2003)
Boulesteix, Anne-Laure, Tutz, Gerhard, Strimmer, Korbinian
Motivation: Cancer diagnosis using gene expression profiles requires supervised learning and gene selection methods. Of the many suggested approaches, the method of emerging patterns (EPs) has the...
Flexible Modelling of Discrete Failure Time Including Time-Varying Smooth Effects (2002)
Discrete survival models have been extended in several ways. More flexible models are obtained by including time-varying coefficients and covariates which determine the hazard rate in an additive but...
Modelling of repeated ordered measurements by isotonic sequential regression (2002)
The paper introduces a simple model for repeated observations of an ordered categorical response variable which is isotonic over time. It is assumed that the measurements represent an irreversible...
Fahrmeir, Ludwig, Tutz, Gerhard
Incluye bibliografía e índice
Vanishing of Risk Factors for the Success and Survival of Newly Founded Companies (2001)
Kauermann, Göran, Tutz, Gerhard
The success of a newly founded company or small business depends on various initial risk factors or staring conditions, respectively, like e.g. the market the business aims for, the experience and...
Generalized semiparametrically structured ordinal models (2001)
Semiparametrically structured models are defined as a class of models for which the predictors may contain parametric parts, additive parts of covariates with an unspecified functional form and...
Generalized semiparametrically structured mixed models (2001)
Generalized linear mixed models are a common tool in statistics which extends generalized linear models to situations where data are hierarchically clustered or correlated. In this article the simple...
Semiparametric Modeling of Ordinal Data (2000)
Kauermann, Göran, Tutz, Gerhard
Parametric models for categorical ordinal response variables, like the proportional odds model or the continuation ratio model, assume that the predictor is given as a linear form of covariates. In...
Semiparametric Modelling of Multicategorical Data (2000)
Parametric multicategorical models are an established tool in statistical data analysis. Alternative semi-parametric models are introduced where part of the explanatory variables is still linearly...
Semiparametric Modeling of Ordinal Data (2000)
Parametric models for categorical ordinal response variables, like the proportional odds model or the continuation ratio model, assume that the predictor is given as a linear form of covariates. In...
Testing Generalized Linear and Semiparametric Models Against Smooth Alternatives (1999)
Kauermann, Göran, Tutz, Gerhard
We propose goodness of fit tests for testing generalized linear models and semiparametric regression models against smooth alternatives. The focus is on models having both, continuous and factorial...
Nonstationary conditional models for spatial data based on varying coefficients (1999)
The analysis of spatial data by means of Markov random fields usually is based on strict stationarity assumptions. Although these assumptions rarely hold, they are necessary in order to obtain...
Comparison between local estimates for multi-categorical varying-coefficent models (1999)
Tutz, Gerhard, Edlich, Silke, Bäumer, Christoph
Varying coefficient models with discrete values of the effect modifier may be estimated by maximum likelihood or weighted least square techniques. We compare bias reduction methods for both estimates...
Varying coefficients in multivariate generalized linear models (1995)
Gerhard Tutz, G Oran Kauermann
The paper deals with multivariate generalized linear models where the parameters may depend on an effect--modifying variable. The local likelihood approach, originally developed by Tibshirani &...
Local likelihood estimation in varying-coefficient models including additive bias correction (1995)
Varying coefficient models result from generalized linear models by allowing the parameter of the linear predictor to vary across some additional explanatory quantity called effect modifier. While...
The kernel method of estimating the cell probabilities of a multivariate categorical distribution, due to Aitchison & Aitken (1976), depends crucially on an unknown smoothing parameter λ. A method...
In this paper R2-type measures of the explanatory power of multivariate linear and categorical probit models proposed in the literature are reviewed and their deficiencies are discussed. It is argued...
Modelling beyond regression functions: an application of multimodal regression to speed-flow data
For speed-flow data, which are intensively discussed in transportation science, common nonparametric regression models of the type "y"="m"("x")+noise turn out to be inadequate since...
Genetic algorithms for the selection of smoothing parameters in additive models
Additive model, Genetic algorithm, Penalized regression splines, B-splines, Improved AIC criterion,
The survival of newly founded firms: a case-study into varying-coefficient models
Göran Kauermann, Gerhard Tutz, Josef Brüderl
The success of a newly founded firm depends on various initial risk factors or start-up conditions such as the market that the business is aiming for, the experience and the age of the founder, the...
Florian Reithinger, Wolfgang Jank, Gerhard Tutz, Galit Shmueli
On-line auctions pose many challenges for the empirical researcher, one of which is the effective and reliable modelling of price paths. We propose a novel way of modelling price paths in eBay's...
Boosting nonlinear additive autoregressive time series
Several methods for the analysis of nonlinear time series models have been proposed. As in linear autoregressive models the main problems are model identification, estimation and prediction. A...