Code
01SC6624
Duration
01 October 2024 → 30 September 2028
Funding
Regional and community funding: Special Research Fund
Promotor
Fellow
Research disciplines
-
Natural sciences
- Statistics not elsewhere classified
- Machine learning and decision making
Keywords
spatio-temporal learning
survey design
climate data
extreme value statistics
Project description
The project aims to develop statistical learning tools to fuse spatio-temporal data from multiple sources and to develop efficient survey designs that exploit sources with different accuracies and coverages. Several modelling strategies will be considered including generalized additive mixed models. Algorithms are validated on climate data sets of China and Europe.