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.

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.

Meta-Analysis Medicine & Life Sciences

Dupuytren Contracture Medicine & Life Sciences

Psychotic Disorders Medicine & Life Sciences

Cohort Studies Medicine & Life Sciences

Randomized Controlled Trials Medicine & Life Sciences

Confidence Intervals Medicine & Life Sciences

Intraclass Correlation Coefficient Mathematics

Prospective Studies Medicine & Life Sciences

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Profiles

Paulo Serra

p.j.serra@tue.nl

Department of Mathematics and Computer Science, Stochastics W&I - Assistant Professor
Department of Mathematics and Computer Science, Statistics

Person: UD : Assistant Professor

Alessandro Di Bucchianico

a.d.bucchianico@tue.nl

Department of Mathematics and Computer Science, Stochastics - Associate Professor
Department of Mathematics and Computer Science, Stochastics W&I - Associate Professor
Department of Mathematics and Computer Science, Statistics

Person: UHD : Associate Professor

Edwin R. van den Heuvel

e.r.v.d.heuvel@tue.nl

Person: HGL : Professor

Research Output 2014 2020

97 Article
5 Conference contribution
5 Review article
3 Report
8 More

Cliques in rank-1 random graphs: the role of inhomogeneity

Bogerd, K., Castro, R. M. & van Der Hofstad, R., Feb 2020, In : Bernoulli. 26, 1, p. 253-285 33 p.

Research output: Contribution to journal › Article › Academic › peer-review

Open Access

Estimation of local degree distributions via local weighted averaging and Monte Carlo cross-validation

Serra, P. & Mandjes, M., Apr 2020, In : Computational Statistics and Data Analysis. 144, 21 p., 106886.

Research output: Contribution to journal › Article › Academic › peer-review

Heterogeneous networks

Degree Distribution

Cross-validation

Averaging

Topology

1 Downloads (Pure)

Latent variable models for harmonization of test scores: a case study on memory

van den Heuvel, E. R., Griffith, L. E., Sohel, N., Fortier, I., Muniz-Terrera, G. & Raina, P., Jan 2020, In : Biometrical Journal. 62, 1, p. 34-52 19 p.

Research output: Contribution to journal › Article › Academic › peer-review

Latent Variable Models

Score Test

Measurement Invariance

Exchangeability

Observational Study