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Research Group Helmut Küchenhoff

Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Statistical Consulting Unit (StaBLab)

C4 | Computational Social Sciences

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)

C4 | Computational Social Sciences

Link to Johannes Piller

Johannes Piller

Statistical Consulting Unit (StaBLab)

C4 | Computational Social Sciences

Publications @MCML

[8]
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)

C4 | Computational Social Sciences

Link to Andreas Bender

Andreas Bender

Dr.

Statistical Learning & Data Science

Coordinator Statistical and Machine Learning Consulting

A1 | Statistical Foundations & Explainability


[7]
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)

C4 | Computational Social Sciences

Maximilian Weigert

Maximilian Weigert

* Former member

C4 | Computational Social Sciences


[6]
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
Maximilian Weigert

Maximilian Weigert

* Former member

C4 | Computational Social Sciences

Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Statistical Consulting Unit (StaBLab)

C4 | Computational Social Sciences


[5]
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 Cornelius Fritz

Cornelius Fritz

Dr.

* Former member

A1 | Statistical Foundations & Explainability

Link to David Rügamer

David Rügamer

Prof. Dr.

Data Science Group

A1 | Statistical Foundations & Explainability

Link to Andreas Bender

Andreas Bender

Dr.

Statistical Learning & Data Science

Coordinator Statistical and Machine Learning Consulting

A1 | Statistical Foundations & Explainability

Maximilian Weigert

Maximilian Weigert

* Former member

C4 | Computational Social Sciences

Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Statistical Consulting Unit (StaBLab)

C4 | Computational Social Sciences

Link to Göran Kauermann

Göran Kauermann

Prof. Dr.

Applied Statistics in Social Sciences, Economics and Business

A1 | Statistical Foundations & Explainability


[4]
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 Cornelius Fritz

Cornelius Fritz

Dr.

* Former member

A1 | Statistical Foundations & Explainability

Maximilian Weigert

Maximilian Weigert

* Former member

C4 | Computational Social Sciences

Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Statistical Consulting Unit (StaBLab)

C4 | Computational Social Sciences

Link to Göran Kauermann

Göran Kauermann

Prof. Dr.

Applied Statistics in Social Sciences, Economics and Business

A1 | Statistical Foundations & Explainability


[3]
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
Maximilian Weigert

Maximilian Weigert

* Former member

C4 | Computational Social Sciences

Link to David Rügamer

David Rügamer

Prof. Dr.

Data Science Group

A1 | Statistical Foundations & Explainability

Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Statistical Consulting Unit (StaBLab)

C4 | Computational Social Sciences


[2]
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

A1 | Statistical Foundations & Explainability

Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Statistical Consulting Unit (StaBLab)

C4 | Computational Social Sciences


[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
Maximilian Weigert

Maximilian Weigert

* Former member

C4 | Computational Social Sciences

Link to Helmut Küchenhoff

Helmut Küchenhoff

Prof. Dr.

Statistical Consulting Unit (StaBLab)

C4 | Computational Social Sciences