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Research Group Helmut Küchenhoff

Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Principal Investigator

Statistical Consulting Unit (StaBLab)

Helmut Küchenhoff

heads the Statistical Consulting Unit (StaBLab) at LMU Munich, which is known for providing expert statistical guidance to both academic researchers and industries.

His research interests include statistical modeling, measurement error, and misclassification, with a focus on applying statistical techniques to real-world data, including the analysis of COVID-19 data.

Team members @MCML

Link to Henri Funk

Henri Funk

Statistical Consulting Unit (StaBLab)

Link to Johannes Piller

Johannes Piller

Statistical Consulting Unit (StaBLab)

Publications @MCML

[16]
C. A. Scholbeck, H. Funk and G. Casalicchio.
Algorithm-Agnostic Feature Attributions for Clustering.
2nd World Conference on Explainable Artificial Intelligence (xAI 2024). Valletta, Malta, Jul 17-19, 2024. DOI.
Abstract

Understanding how assignments of instances to clusters can be attributed to the features can be vital in many applications. However, research to provide such feature attributions has been limited. Clustering algorithms with built-in explanations are scarce. Common algorithm-agnostic approaches involve dimension reduction and subsequent visualization, which transforms the original features used to cluster the data; or training a supervised learning classifier on the found cluster labels, which adds additional and intractable complexity. We present FACT (feature attributions for clustering), an algorithm-agnostic framework that preserves the integrity of the data and does not introduce additional models. As the defining characteristic of FACT, we introduce a set of work stages: sampling, intervention, reassignment, and aggregation. Furthermore, we propose two novel FACT methods: SMART (scoring metric after permutation) measures changes in cluster assignments by custom scoring functions after permuting selected features; IDEA (isolated effect on assignment) indicates local and global changes in cluster assignments after making uniform changes to selected features.

MCML Authors
Link to Christian Scholbeck

Christian Scholbeck

Statistical Learning & Data Science

Link to Henri Funk

Henri Funk

Statistical Consulting Unit (StaBLab)

Link to Giuseppe Casalicchio

Giuseppe Casalicchio

Dr.

Statistical Learning & Data Science


[15]
J. Ramjith, A. Bender, K. C. B. Roes and M. A. Jonker.
Recurrent events analysis with piece-wise exponential additive mixed models.
Statistical Modelling 24.3 (Jun. 2024). DOI.
MCML Authors
Link to Andreas Bender

Andreas Bender

Dr.

Machine Learning Consulting Unit (MLCU)


[14]
W. H. Hartl, P. Kopper, L. Xu, L. Heller, M. Mironov, R. Wang, A. G. Day, G. Elke, H. Küchenhoff and A. Bender.
Relevance of Protein Intake for Weaning in the Mechanically Ventilated Critically Ill: Analysis of a Large International Database.
Critical Care Medicine 50.3 (Mar. 2024). DOI.
Abstract

The association between protein intake and the need for mechanical ventilation (MV) is controversial. We aimed to investigate the associations between protein intake and outcomes in ventilated critically ill patients.

MCML Authors
Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Statistical Consulting Unit (StaBLab)

Link to Andreas Bender

Andreas Bender

Dr.

Machine Learning Consulting Unit (MLCU)


[13]
D. Wolffram, S. Abbott, M. an der Heiden, S. Funk, F. Günther, D. Hailer, S. Heyder, T. Hotz, J. van de Kassteele, H. Küchenhoff, S. Müller-Hansen, D. Syliqi, A. Ullrich, M. Weigert, M. Schienle and J. Bracher.
Collaborative nowcasting of COVID-19 hospitalization incidences in Germany.
PLOS Computational Biology 19.8 (Aug. 2023). DOI.
MCML Authors
Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Statistical Consulting Unit (StaBLab)


[12]
C. Reinkemeyer, Y. Khazaei, M. Weigert, M. Hannes, R. Le Gleut, M. Plank, S. Winter, I. Norena, T. Meier, L. Xu, R. Rubio-Acero, S. Wiegrebe, T. G. Le Thi, C. Fuchs, K. Radon, I. Paunovic, C. Janke, A. Wieser, H. Küchenhoff, M. Hoelscher, N. Castelletti and K. I. O. W. G. KoCo Impf ORCHESTRA Working Grp.
The Prospective COVID-19 Post-Immunization Serological Cohort in Munich (KoCo-Impf): Risk Factors and Determinants of Immune Response in Healthcare Workers.
Viruses 15.7 (Jul. 2023). DOI.
MCML Authors
Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Statistical Consulting Unit (StaBLab)


[11]
C. Fritz, G. De Nicola, F. Günther, D. Rügamer, M. Rave, M. Schneble, A. Bender, M. Weigert, R. Brinks, A. Hoyer, U. Berger, H. Küchenhoff and G. Kauermann.
Challenges in Interpreting Epidemiological Surveillance Data – Experiences from Germany.
Journal of Computational and Graphical Statistics 32.3 (Dec. 2022). DOI.
MCML Authors
Link to David Rügamer

David Rügamer

Prof. Dr.

Data Science Group

Link to Andreas Bender

Andreas Bender

Dr.

Machine Learning Consulting Unit (MLCU)

Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Statistical Consulting Unit (StaBLab)

Link to Göran Kauermann

Göran Kauermann

Prof. Dr.

Applied Statistics in Social Sciences, Economics and Business


[10]
E. Pretzsch, V. Heinemann, S. Stintzing, A. Bender, S. Chen, J. W. Holch, F. O. Hofmann, H. Ren, F. Böschand, H. Küchenhoff, J. Werner and W. K. Angele.
EMT-Related Genes Have No Prognostic Relevance in Metastatic Colorectal Cancer as Opposed to Stage II/III: Analysis of the Randomised, Phase III Trial FIRE-3 (AIO KRK 0306; FIRE-3).
Cancers 14.22 (Nov. 2022). DOI.
MCML Authors
Link to Andreas Bender

Andreas Bender

Dr.

Machine Learning Consulting Unit (MLCU)

Link to Shuo Chen

Shuo Chen

Database Systems & Data Mining

Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Statistical Consulting Unit (StaBLab)


[9]
R. Sonabend, A. Bender and S. Vollmer.
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures.
Bioinformatics 38.17 (Sep. 2022). DOI.
MCML Authors
Link to Andreas Bender

Andreas Bender

Dr.

Machine Learning Consulting Unit (MLCU)


[8]
C. Fritz, G. De Nicola, M. Rave, M. Weigert, Y. Khazaei, U. Berger, H. Küchenhoff and G. Kauermann.
Statistical modelling of COVID-19 data: Putting generalized additive models to work.
Statistical Modelling 24.4 (Aug. 2022). DOI.
MCML Authors
Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Statistical Consulting Unit (StaBLab)

Link to Göran Kauermann

Göran Kauermann

Prof. Dr.

Applied Statistics in Social Sciences, Economics and Business


[7]
M. Mittermeier, M. Weigert, D. Rügamer, H. Küchenhoff and R. Ludwig.
A deep learning based classification of atmospheric circulation types over Europe: projection of future changes in a CMIP6 large ensemble.
Environmental Research Letters 17.8 (Jul. 2022). DOI.
MCML Authors
Link to David Rügamer

David Rügamer

Prof. Dr.

Data Science Group

Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Statistical Consulting Unit (StaBLab)


[6]
A. Bauer, M. Weigert and H. Jalal.
APCtools: Descriptive and Model-based Age-Period-Cohort Analysis.
The Journal of Open Source Software 7.73 (May. 2022). DOI.
Abstract

Age-Period-Cohort (APC) analysis aims to determine relevant drivers for long-term develop- ments and is used in many fields of science (Yang & Land, 2013). The R package APCtools offers modern visualization techniques and general routines to facilitate the interpretability of the interdependent temporal structures and to simplify the workflow of an APC analysis. Separation of the temporal effects is performed utilizing a semiparametric regression approach. We shortly discuss the challenges of APC analysis, give an overview of existing statistical software packages and outline the main functionalities of the package.

MCML Authors

[5]
A. Bauer.
Flexible approaches in functional data and age-period-cohort analysis with application on complex geoscience data.
Dissertation 2022. DOI.
Abstract

This dissertation develops new approaches for robustly estimating functional data structures and analyzing age-period-cohort (APC) effects, with applications in seismology and tourism science. The first part introduces a method that separates amplitude and phase variation in functional data, adapting a likelihood-based registration approach for generalized and incomplete data, demonstrated on seismic data. The second part presents generalized functional additive models (GFAMs) for analyzing associations between functional data and scalar covariates, along with practical guidelines and an R package. The final part addresses APC analysis, proposing new visualization techniques and a semiparametric estimation approach to disentangle temporal dimensions, with applications to tourism data, and is supported by the APCtools R package. (Shortened.)

MCML Authors

[4]
A. Python, A. Bender, M. Blangiardo, J. B. Illian, Y. Lin, B. Liu, T. C. D. Lucas, S. Tan, Y. Wen, D. Svanidze and J. Yin.
A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales.
Journal of the Royal Statistical Society. Series A (Statistics in Society) 185.1 (Jan. 2022). DOI.
MCML Authors
Link to Andreas Bender

Andreas Bender

Dr.

Machine Learning Consulting Unit (MLCU)


[3]
A. Bauer, F. Scheipl and H. Küchenhoff.
Registration for Incomplete Non-Gaussian Functional Data.
Preprint at arXiv (Aug. 2021). arXiv.
MCML Authors
Link to Fabian Scheipl

Fabian Scheipl

PD Dr.

Functional Data Analysis

Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Statistical Consulting Unit (StaBLab)


[2]
A. Python, A. Bender, A. K. Nandi, P. A. Hancock, R. Arambepola, J. Brandsch and T. C. D. Lucas.
Predicting non-state terrorism worldwide.
Science Advances 7.31 (Jul. 2021). DOI.
MCML Authors
Link to Andreas Bender

Andreas Bender

Dr.

Machine Learning Consulting Unit (MLCU)


[1]
M. Weigert, A. Bauer, J. Gernert, M. Karl, A. Nalmpatian, H. Küchenhoff and J. Schmude.
Semiparametric APC analysis of destination choice patterns: Using generalized additive models to quantify the impact of age, period, and cohort on travel distances.
Tourism Economics 28.5 (Jan. 2021). DOI.
Abstract

This study investigates how age, period, and birth cohorts are related to altering travel distances. We analyze a repeated cross-sectional survey of German pleasure travels for the period 1971–2018 using a holistic age–period–cohort (APC) analysis framework. Changes in travel distances are attributed to the life cycle (age effect), macro-level developments (period effect), and generational membership (cohort effect). We introduce ridgeline matrices and partial APC plots as innovative visualization techniques facilitating the intuitive interpretation of complex temporal structures. Generalized additive models are used to circumvent the identification problem by fitting a bivariate tensor product spline between age and period. The results indicate that participation in short-haul trips is mainly associated with age, while participation in long-distance travel predominantly changed over the period. Generational membership shows less association with destination choice concerning travel distance. The presented APC approach is promising to address further questions of interest in tourism research.

MCML Authors
Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Statistical Consulting Unit (StaBLab)