Abstract:
|
A multiplicity adjustment is proposed for multiple testing problems that is specified through the discrete prior, i.e., the prior model probabilities. The proposed scheme makes use of a graphical nesting structure between models to explore asymptotic consistency of model choice, with respect to simultaneous increases in sample size and dimensionality. From this viewpoint, variable selection is shown to represent an interesting balance point among multiple testing problems. Moreover, the scheme identifies performance improvements over standard variable selection priors, such as beta-binomial priors, and establishes asymptotic consistence in "ultra-high" dimensions. Criteria for asymptotic consistency in partition-based clustering problems are also explored, and point to a new prior specification that induces good performance. Issues of interpretation with regard to the discrete prior's representation of subjective knowledge are also discussed.
|
ASA Meetings Department
732 North Washington Street, Alexandria, VA 22314
(703) 684-1221 • meetings@amstat.org
Copyright © American Statistical Association.