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1. GRK 2624
2. Research
Projects

Research Projects REGRESSION: High-dimensional regression methods for gene-exposure interactions The central goal in this project area is the modelling of health indicators with modern regression methods. The [...] investigated. The models considered comprise classical linear and logistic regression as well as modern procedures such as logic regression, regularised procedures and generalised additive models. For selecting [...] techniques will be adapted. R1 High-dimensional regression for screening of important genetic and environmental factors (Ickstadt, Schikowski) R2 Regression with nonlinear modelling of metric environmental …
1. STATML
2. Research
Publications

Additive Distributional Regression Models pdf A. Strömer, N. Klein, C. Staerk, H. Klinkhammer and A. Mayr, 2022 Submitted to Statistics in Medicine. Distributional Adaptive Soft Regression Trees pdf N. Umlauf [...] Quantile Regression with an Analysis to Air Pollution Thresholds pdf N. Klein and J. Mateu, 2021 R& R Annals of Applied Statistics Flexible Specification Testing in Semi-Parametric Quantile Regression Models [...] Exposures: Bayesian Distributional Regression with Copulas pdf J. Rathjens, A. Kolbe, J. H ̈olzer, K. Ickstadt and N. Klein, 2021 Semi-structured Deep Distributional Regression: Combining Structured Additive …
1. Fakultät Statistik
2. News: 8/27/2025
New R-package PrInDT, version 2.0.1 (Authors: C. Weihs, S. Buschfeld, TU Dortmund)

PrInDTMulev() deal with classification problems. The function PrInDTreg() has been developed for regression problems. NEW in version 2: A second kind of subsampling called “structured sampling” is implemented [...] implemented in the functions PrInDTCstruc() and PrInDTRstruc() for classification and regression. In these functions, repeated measurements data can be analyzed, too. Simultaneous modeling The function PrI [...] multilabel classification. NEW in version 2: Multilabel 2-stage versions of classification and regression trees are implemented in the functions C2SPrInDT() and R2SPrInDT() as well as interdependent multilabel …
1. GRK 2624
2. Research
3. Projects
I5: Omics-RegInt

I5: Omics-RegInt Integration of different omics data with regression methods In project I5 (Integration of different omics data with regression methods) , the effect of toxicological compounds on tissues [...] multinomial sections of the DiPa plot as target variable. We plan to refine this regression approach, e.g. by adapting copula regression models. Further, in this project different data sources will be considered [...] classes of Generalised Linear or Additive Regression Models (GLMs; GAMs) are useful, covering, e.g., logistic, multinomial, Poisson, and ordinary linear regression. Depending on the dimensionality of the …
1. STATML
2. Curriculum Vitae
Conferences and Presentations

Distributional Regression and Beyond at the Research Center Trustworthy Data Science and Security (Technische Universität Dortmund, 20 - 21/09/2021) Marginally Calibrated Deep Distributional Regression (with an [...] Advances in Distributional Regression - Statistical Learning Approaches at the Faculty of Business and Economics (Georg-August-Universität Göttingen, 12/07/2020) Bayesian Regression Copulas at the Mathematical [...] ät Göttingen, 13 - 18/07/2014) Organized Sessions Organized Session: Recent Advances in Copula Regression at the 15th International Conference of the ERCIM WG on Computational and Methodological Statistics …
1. BD
2. Chair
3. Former academic staff
Dr. Hendrik van der Wurp

to Generalised Joint Regression Modelling for Count Data, CMStatistics 2021, 18 December 2021, London, United Kingdom. Introducing Regularisation to Generalised Joint Regression Modelling for Count Data [...] Data Regression Framework for Modelling Football Scores (and Introducing Regularisation to it), IWSM 2021, 20 July 2021, Bilbao, Spain. Introducing Regularisation to Generalised Joint Regression Modelling [...] via generalised joint regression modelling, DStatG, 09 September 2019, Trier, Germany. Predicting matches in international football tournaments via generalised joint regression modelling, PhD-colloquium …
1. GRK 2624
2. Research
3. Projects
R1: GenEnv-HDReg

R1: GenEnv-HDReg High-dimensional regression for screening of important genetic and environmental factors In project R1 (High dimensional regression for screening of important genetic and environmental [...] as part of a regression analysis. In principle, this variable selection problem cannot be solved efficiently (see Foster et al., 2015). However, efficient heuristics such as LASSO regression can be formulated [...] l factors), we are developing variable selection methods for regression models. The methods can be used to screen for important genetic and environmental risk factors and their interactions. Although the …
1. RCS
2. News: 7/15/2024
Poster @ L4DC 2024

present his poster on paper “Piecewise regression via mixed-integer programming for MPC”. It contains a new method for finding global optima in piecewise regression problems, which is subsequently applied [...] Dieter for further details or have a look in the paper: D. Teichrib, and M. Schulze Darup. Piecewise regression via mixed-integer programming for MPC , 10th International Conference on Control, 6th Annual Learning …
1. GRK 2624
2. Research
3. Projects
R3: Gen-InterAna

2624, in project R3, we have investigated the performance of tree-based regression and classification methods such as logic regression (Ruczinski et al., 2001) and Random Forests (Breiman et al., 2001) in [...] marginal effects, we have developed a new tree-based regression and classification method called logicDT (Lau et al., 2024). Similar to the logic regression-based logic bagging (Schwender and Ickstadt, 2008) [...] Logic Regression. Journal of Computational and Graphical Statistics 12, 475-511. doi: 10.1198/1061860032238 Schwender H , Ickstadt K (2008): Identification of SNP interactions using logic regression. Bi …
1. Compstat
2. Research
3. Projects
Intuitive Joint Priors for Bayesian Multilevel Models

Intuitive Joint Priors for Bayesian Multilevel Models Regression models are ubiquitous in the quantitative sciences making up a big part of all statistical analysis performed on data. In the quantitative [...] specifically to account for the nested structure in multilevel data and are a widely applied class of regression models. From a Bayesian perspective, the widespread success of MLMs can be explained by the fact [...] priors for each parameter. However, in almost all state-of-the-art approaches, different additive regression terms in MLMs, corresponding to different parameter sets, still receive mutually independent priors …

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