Biography
Michel van de Velden is an associate Professor of Statistics at the Econometric Institute of the Erasmus University Rotterdam. His main research interests are exploratory data analysis. In particular, dimension reduction and cluster analysis methods with a strong focus on data visualization. In addition, he is in involved in several supervised machine learning projects involving tree-based machine learning methods.
More information
Work
- Michel van de Velden, Alfonso Iodice D’Enza, Angelos Markos & Carlo Cavicchia (2024) - Unbiased mixed variables distance - [link]
- Sabine Knapp & Michel van de Velden (2024) - Predicting inspection outcomes and evaluating port state control targeting using random forests - doi: 10.13140/RG.2.2.26683.43040
- Rick S.H. Willemsen, Michel van de Velden & Wilco van den Heuvel (2024) - On the uniqueness of correspondence analysis solutions - Linear Algebra and Its Applications, 690, 162-185 - doi: 10.1016/j.laa.2024.03.014 - [link]
- Michel van de Velden, Alfonso Iodice D’Enza, Angelos Markos & Carlo Cavicchia (2024) - A general framework for implementing distances for categorical variables - Pattern Recognition, 153 - doi: 10.1016/j.patcog.2024.110547 - [link]
- Sabine Knapp & Michel van de Velden (2024) - Improved risk predictions of vessels using machine learning: how effective is the status quo?
- Rick S.H. Willemsen, Wilco van den Heuvel & Michel van de Velden (2023) - A new mixed integer programming approach for inverse correspondence analysis - Computers and Operations Research, 160 - doi: 10.1016/j.cor.2023.106375 - [link]
- Anna Torres, Leonor Vacas de Carvalho, Joana Cesar Machado, Michel van de Velden & Patrício Costa (2023) - Exploring consumer segments defined by affective responses to naturalness in logo design - Journal of Product and Brand Management, 32 (8), 1287-1305 - doi: 10.1108/JPBM-06-2022-4023 - [link]
- Rosaria Lombardo, Michel van de Velden & Eric J. Beh (2023) - Three-Way Correspondence Analysis in R - R Journal, 15 (2), 237-262 - doi: 10.32614/RJ-2023-049 - [link]
- Sabine Knapp & Michel van de Velden (2023) - Exploration of machine learning methods for maritime risk predictions - Maritime Policy and Management, 51 (7), 1443-1473 - doi: 10.1080/03088839.2023.2209788 - [link]
- Sabine Knapp & Michel van de Velden (2022) - Predicting detention and deficiencies using random forests
EQI
- Start date approval
- July 2022
- End date approval
- July 2025
- Place
- ROTTERDAM
Erasmus Academie
- Start date approval
- July 2022
- End date approval
- July 2025
- Place
- ROTTERDAM
Applied Statistics 1
- Year
- 2024
- Course Code
- FEB11005X
Seminar Data Science for Marketing Analy
- Year
- 2024
- Course Code
- FEM11152
Applied Statistics 1
- Year
- 2024
- Course Code
- FEB11005
