Modern applied and theoretical approaches to environmental statistics
Organiser
Participants
Conference
Proposal Description
In the face of rapid environmental changes and increasing data complexity, modern approaches to environmental statistics are essential for accurate analysis and informed decision-making. This invited session will showcase different innovative methodologies and their applications in environmental science. Topics will include methods for skew multivariate spatial distributions, functional time series model for particulate matter forecasting, machine learning for sea ice prediction, and data fusion for satellite imagery.
Submissions
- Functional time series forecasting with dynamic updating: An application to intraday particulate matter concentration
- Ice sea the future: statistical machine learning to predict Antarctic sea ice with quantified uncertainty
- SAR – Multispectral data fusion with higher order singular value decomposition
- Skewed multivariate distributions for spatial data and their extreme-value limits