Title
Group Variable Selection via Hierarchical Lasso and Its Oracle Property
Speaker
Ji Zhu, University of Michigan
Abstract
In many engineering and scientific applications, predictor variables are grouped, for example, in biological applications where assayed genes or proteins can be grouped by biological role. Common statistical analysis methods such as ANOVA, factor analysis, and functional modeling with partially ordered basis sets also exhibit natural variable groupings. We develop a new variable selection method that respects the group constraint. Our new method enjoys benefits that existing successful methods do not have, while offering the potential for achieving theoretical "oracle" properties.
Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.
