Sufficient Dimension Reduction for Multi-Populations

• Activity Number: 131
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Xuerong Wen*+ and Tao Wang and Lixing Zhu
• Companies: Missouri University of Science & Technology and Hongkong Baptist University and Hong Kong Baptist University
• Keywords: Multiple Population ; Sufficient Dimension Reduction ; Sliced Inverse Regression ; Partial Central Subspace

Abstract

In this paper, we propose a novel dimension reduction method for multi-population data. Our method is the first one in the area which could conduct a joint analysis while still retaining the population specific effects. Though partial dimension reduction (Chiaromonte et al., 2002) can be adopted to deal with multi-population dimension reduction, it encloses the related directions for all populations, population-specific effects are ignored. On the other side, unlike the conditional analsysis which is carried out separately within each individual population, our method makes use of the information across the multiple populations which greatly improve the estimation accuracy. Simulations and a real data example were given to illustrate our methodology.