September 23, 2009

Next Week at the Statistics Seminar: Selecting Demanding Models

Attention conservation notice: Only of interest if you (1) care about statistical model selection and (2) are in Pittsburgh on Monday afternoon.
"Composite Likelihood Bayesian Information Criteria for Model Selection in High-Dimensional Data"
Prof. Peter Song, Dept. of Biostatistics, University of Michigan
Abstract: For high-dimensional data with complicated dependency structures, the full likelihood approach often renders to intractable computational complexity. This imposes difficulty on model selection as most of the traditionally used information criteria require the evaluation of the full likelihood. We propose a composite likelihood version of the Bayesian information criterion (BIC) and establish its consistency property for the selection of the true underlying model. Under some mild regularity conditions, the proposed BIC is shown to be selection consistent, where the number of potential model parameters is allowed to increase to infinity at a certain rate of the sample size. Simulation studies demonstrate the empirical performance of this new BIC criterion, especially for the scenario that the number of parameters increases with the sample size.
Place and Time: 4--5 pm on Monday, 28 September 2009, in Doherty Hall A310

As always, the seminar is free and open to the public.

Enigmas of Chance

Posted at September 23, 2009 16:53 | permanent link

Three-Toed Sloth