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