Dr. Qu will describe a heterogeneous modeling framework which simultaneously achieves individual-wise feature selection and individualized covariates' effects subgrouping. In contrast to conventional model selection approaches, the new approach constructs a separation penalty with multi-directional shrinkages, which facilitates individualized modeling to distinguish strong signals from noisy ones and selects different relevant variables for different individuals. Meanwhile, the proposed model identifies subgroups among which individuals share similar covariates' effects, and thus improves individualized estimation efficiency and feature selection accuracy. Qu provides a general theoretical foundation under a double-divergence modeling framework where the number of individuals and the number of individual-wise measurements can both diverge, which enable inference on both an individual level and a population level. The individualized estimator has strong oracle property to ensure its optimal large sample property under various conditions.
Speaker: Annie Qui, PhD, Chancellor's Professor, Department of Statistics, UC Irvine
Register: http://eepurl.com/g1X35P