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11:00 - "The Pivotal Information Criterion" -
The Pivotal Information Criterion
Abstract:
The Bayesian and Akaike information criteria aim at finding a good balance between under- and over-fitting. They are extensively used everyday by practitioners. Yet we content they suffer from three afflictions: their inherent (best subset) discrete optimization is infeasible beyond moderate dimension, their need for estimation of a nuisance parameter makes them inefficient in high dimension, and their penalty parameter λ = log n and λ = 2 are too small for feature detection. We alleviate these issues with the pivotal information criterion.