Connectionists: LASSO-Patternsearch paper

[Grace Wahba]

Available at http://www.stat.wisc.edu/~wahba -> TRLIST

W. Shi, G. Wahba, S. Wright, K. Lee, R. Klein and B. Klein.   
LASSO-Patternsearch Algorithm with Applications to 
Ophthalmology and Genomic Data "  February 2008.

To appear, Statistics and Its Interface(SII)

The LASSO-Patternsearch algorithm is proposed to efficiently
identify patterns of multiple dichotomous risk factors for outcomes 
of interest in  demographic and genomic studies.  The patterns 
considered are those that arise naturally from the log linear 
expansion of the multivariate Bernoulli density. The method is  
designed for the case where there is a possibly very large number
of candidate patterns but it is believed that only a relatively  
small number are important. A LASSO is used to greatly reduce the 
number of candidate patterns, using a novel computational algorithm
that can handle an extremely large number of unknowns simultaneously.
The patterns surviving the LASSO are further pruned in the framework 
of (parametric) generalized linear models. A novel tuning procedure  
based on the GACV for Bernoulli outcomes, modified to act as a model 
selector, is used at both steps. We applied the method to myopia data 
from the population-based Beaver Dam Eye Study, exposing physiologically 
interesting interacting risk factors. We then applied the 
the method to data from a generative model of Rheumatoid Arthritis
based on  Problem 3 from the Genetic Analysis Workshop 15, successfully
demonstrating its potential to efficiently recover higher order 
patterns from attribute vectors of length typical of genomic studies.