Content available in repository
Content available in repository
Organization profile
Introduction / mission
The Statistics group develops and compares data-analytical methods for analyzing and sampling complex structured correlated data sets. It includes parameter estimation, model fitting, latent variable models, mixed models, missing data, statistical process control, survival & reliability theory, time series analysis, and statistical learning methods.
One of the central themes is the analysis of high-dimensional temporal data sets and other large data sets. The group actively explores new research lines in Data Science and maintains many strong ties with industry, including biopharmaceutical companies, chemical industry, medical centers and international research institutes.
Highlighted phrase
The Statistics group develops and compares data-analytical methods for analyzing and sampling complex structured correlated data sets.
Organisational profile
Success stories
Framingham Heart Study A long-term longitudinal data set on more than 5000 participants (started at 1948) has been brought to the TU/e to collaborate with Boston University on new statistical methods.
Strong collaboration with the Academic partners of Maelstrom Research a leading international institute on harmonization of data from multiple cohort studies.
Industrial collaboration with pharmaceutical industry on validation and implementation of rapid microbiological methods to test medicinal products and processes.
Continuous Personal Health as part of the Philips flagship Data Science , is currently developing new methods for monitoring heart characteristics and sleep.
We are leading a Big Data & Reliability Platform together with industrial partners.
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. Our work contributes towards the following SDG(s):
Fingerprint
Dive into the research topics where Statistics is active. These topic labels come from the works of this organisation's members. Together they form a unique fingerprint.
Network
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Profiles
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Osama Almalik, MSc
- Mathematics and Computer Science, Statistics - Doctoral Candidate
Person: Prom. : doctoral candidate (PhD)
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Bartosz Animucki
- Mathematics and Computer Science, Statistics - Doctoral Candidate
Person: Prom. : doctoral candidate (PhD)
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A normal-probit-binomial model for the analysis of repeated sum scores from multi-item questionnaires
Nooraee, N., Molenberghs, G., Ormel, J. & van den Heuvel, E. R., 2022, (Accepted/In press) In: Communications in Statistics: Simulation and Computation. XX, XResearch output: Contribution to journal › Article › Academic › peer-review
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Differences in estimates for 10-year risk of cardiovascular disease in Black versus White individuals with identical risk factor profiles using pooled cohort equations: an in silico cohort study
Vasan, R. S. & van den Heuvel, E., Jan 2022, In: The Lancet Digital Health. 4, 1, p. e55-e63Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile2 Citations (Scopus)2 Downloads (Pure) -
D-Optimal Designs for the Mitscherlich Non-Linear Regression Function
Heidari, M., Manju, M. A., IJzerman-Boon, P. C. & van den Heuvel, E. R., Mar 2022, In: Mathematical Methods of Statistics. 31, 1, p. 1-17 17 p.Research output: Contribution to journal › Article › Academic › peer-review
1 Downloads (Pure)
Datasets
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How did governmental interventions affect the spread of COVID-19 in European countries?
Post, R. A. J. (Contributor), Regis, M. (Contributor), Zhan, Z. (Contributor) & van den Heuvel, E. R. (Contributor), Figshare Academic Research System, 7 Feb 2021
DOI: 10.6084/m9.figshare.c.5321186
Dataset
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A comparison of methods for clustering longitudinal data with slowly changing trends
van den Heuvel, E. R. (Contributor), Pauws, S. C. (Contributor) & den Teuling, N. G. P. (Contributor), Figshare Academic Research System, 29 Sep 2021
DOI: 10.6084/m9.figshare.13611385
Dataset
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Simulation data for "Increasing efficacy of contact-tracing applications by user referrals and stricter quarantining"
Jorritsma, J. (Contributor), Komjathy, J. (Contributor), Lapinskas, J. (Contributor) & Goldberg, L. A. (Contributor), Zenodo, 12 Mar 2021
Dataset
Activities
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Identifying different types of companies via their website texts.
Piet Daas (Speaker)
10 Sep 2021Activity: Talk or presentation types › Keynote talk › Scientific
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Machine Learning from a Statistical Perspective
Marco Puts (Speaker) & Piet Daas (Speaker)
11 May 2021Activity: Talk or presentation types › Keynote talk › Professional
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Survey and Big Data interactions
Piet Daas (Speaker) & Marco Puts (Speaker)
4 Nov 2020Activity: Talk or presentation types › Keynote talk › Professional
Press / Media
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ICMS Fellowship for Richard Post
Remco W. van der Hofstad & Edwin R. van den Heuvel
16/05/22
1 item of Media coverage
Press/Media: Expert Comment
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Boston University School of Public Health: Replacing the Term 'Race' in Risk Predictions for Heart Attacks Among Black Patients
4/02/22
1 item of Media coverage
Press/Media: Expert Comment
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Improving Risk Prediction for Chronic Disease Management
28/12/21
1 item of Media coverage
Press/Media: Expert Comment
Student theses
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Adaptive label selection for online prediction with expert advice
Author: van Eersel, M., 11 May 2020Supervisor: Castro, R. M. (Supervisor 1)
Student thesis: Master
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A Mathematical and Simulation-based Analysis of Weighted Linear Regression
Author: Prikken, L. H. A., 27 Jan 2020Supervisor: van den Heuvel, E. R. (Supervisor 1) & Regis, M. (Supervisor 2)
Student thesis: Master
File -
A method for evaluating digitally drawn functions with applications in high school education
Author: Chu, V., 2 Oct 2019Supervisor: Castro, R. M. (Supervisor 1)
Student thesis: Bachelor
File