A1726
Title: Nonconvex-regularized integrative sufficient dimension reduction for multi-source data
Authors: Shanshan Ding - University of Delaware (United States) [presenting]
Abstract: As advances in high-throughput technology significantly expand data availability, integrative analysis of multiple data sources has become an increasingly important tool for biomedical studies. An integrative and nonconvex-regularized sufficient dimension reduction method is proposed to achieve simultaneous dimension reduction and variable selection for multi-source data analysis in high dimensions. The proposed method aims to extract sufficient information in a supervised fashion, and the asymptotic results establish a new theory for integrative sufficient dimension reduction and allow the number of predictors in each data source to increase exponentially fast with sample size. The promising performance of the integrative estimator and efficient numerical algorithms is demonstrated through simulation and real data examples.
