Important: Note that this guest talk is on Wednesday 10.00-11.00
Title
Data Integration Via Analysis of Subspaces (DIVAS)
Abstract
A major challenge in the age of Big Data is the integration of disparate data types into a single data analysis. That is tackled here in the context of data blocks measured on a common set of experimental cases. Joint variation is defined in terms of modes of variation having identical scores across data blocks. That allows mathematically rigorous formulation of individual variation within each data block in terms of individual modes. These are mathematically defined through modes of variation with common scores. DIVAS improves earlier methods using a novel random direction approach to statistical inference, and by treating partially shared blocks. Usefulness is illustrated using mortality, cancer and neuroimaging data sets.
Bio
Professor J. S. Marron (Steve Marron), Department of Statistics and O.R., School of Data Science and Society, University of North Carolina
Speaker will present in person.
Practical
Integreat guest talks are open for attendance for everybody.
For those unable to attend in person: https://uio.zoom.us/j/62535744013
Point of contacts:
- Organiser and moderator: Professor Ingrid K. Glad, University of Oslo
- Integreat Administrative Leader Maria Dikova, University of Oslo