STATISTICS COLLOQUIUM
Jim Booth, Professor
Cornell University
Department of Biological Statistics and
Computational Biology
Table counting and exact conditional inference for contingency tables
ABSTRACT
I will review exact conditional inference in the context of loglinear models for contingency tables. The feasibility of exact inference often depends on the ability to enumerate a reference set determined by sufficient statistics which impose linear constraints on the contingency table counts. A double-saddlepoint approximation is proposed for determining the number tables with counts satisfying these linear constraints. Computation of the approximation involves fitting a generalized linear model for geometric responses which can be accomplished almost instantaneously using the iterated weighted least squares algorithm. The approximation is far superior to other analytical approximations that have been proposed, and is shown to be highly accurate in a range of examples, including some for which analytical approximations were previously unavailable. A similar approximation is proposed for tables consisting of only zeros and ones based on a logistic regression model. A higher order adjustment to the basic double saddlepoint further improves the accuracy of the approximation in almost all cases.
DATE: Wednesday, December 7, 2016
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 105
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu