Classified Mixed Model Prediction

• Activity Number: 226
• Type: Invited
• Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
• Sponsor: International Indian Statistical Association
• Author(s): Thuan Nguyen* and Jiming Jiang and J. Sunil Rao
• Companies: Oregon Health & Science University and UC Davis and University of Miami
• Keywords: CMMP ; mixed effects ; prediction ; simulation ; subject-level ; theory

Abstract

Mixed model prediction (MMP) has a fairly long history starting with Henderson's early work in animal breeding (Henderson 1948). The field has since flourished thanks to its broad applications in such fields as genetics, agriculture, education, and surveys (e.g., Robinson 1991). Nowadays, new and challenging problems have emerged, to which methods of MMP are potentially applicable, but not without further methodological and computational developments. For the most part, such problems occur when interest is at subject level (e.g., personalized medicine), or (small) sub-population level (e.g., community center). The challenges have to do with better prediction of a mixed effect, or a future observation, by identifying the class that a new subject belongs to. We propose a new method, called classified mixed model prediction (CMMP), to solve this problem. We develop a theory for CMMP and investigate its empirical performance through simulation studies and a real-data application.