James Stephen Marron, University of North Carolina
Data Integration Via Analysis of Subspaces
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.
about the speaker
Professor Marron is widely recognized as a world research leader in the statistical disciplines of high dimensional, functional, and object-oriented data analysis, and data visualization. He has made broad major contributions ranging from the invention of innovative new statistical methods, through software development, and on to statistical and mathematical theory. His research continues with a number of ongoing deep, interdisciplinary research collaborations with colleagues in Computer Science, Genetics, Medicine, Mathematics and Biology.