Information Geometry of Binomial Mixtures

• Activity Number: 507
• Type: Topic Contributed
• Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
• Sponsor: SSC
• Author(s): Paul Marriott+ and Zhiyue Robin Huang*
• Companies: University of Waterloo and University of Waterloo
• Keywords: Information geometry ; binomial models ; mixtures

Abstract

Local mixture models have proved useful in many statistical applications. This talk looks at ways in which the local assumption, which is used in an asymptotic approximation, can be relaxed in order to generate a much larger class of models which still have the very attractive geometric and inferential properties of local mixture models. The tool used to develop this large class of models is the Karhunen-Loeve decomposition. Computational issues associated with working with these models are also briefly considered. The talk concentrates on the case of mixtures of binomial models both as illustration of general theory and for their applicability.