Portland State University
Fariborz Maseeh Department of Mathematics & Statistics
The Maseeh Mathematics and Statistics Colloquium Series
~Department of Statistics,
University of California at Davis ~
INVISIBLE FENCE METHODS AND THE IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENE SETS
The fence method (Jiang et al. 2008; Ann. Statist. 36, 1669-1692) is a recently developed strategy for model selection. The idea involves a procedure to isolate a subgroup of what are known as correct models (of which the optimal model is a member). This is accomplished by constructing a statistical fence, or barrier, to carefully eliminate incorrect models. Once the fence is constructed, the optimal model is selected from amongst those within the fence according to a criterion which can be made flexible. The construction of the fence can be made adaptively to improve finite sample performance. We extend the fence method to situations where a true model may not exist or among the candidate models. Furthermore, another look at the fence methods leads to a new procedure, known as invisible fence (IF). A fast algorithm is developed for IF in the case of subtractive measure of lack-of-fit. The main application considered here is microarray gene set analysis. In particular, Efron and Tibshirani (2007; Ann. Appl. Statist. 1, 107 129) proposed a gene set analysis (GSA) method based on testing the significance of gene-sets. In typical situations of microarray experiments the number of genes is much larger than the number of microarrays. This special feature presents a real challenge to implementation of IF to microarray gene-set analysis. We show how to solve this problem, and carry out an extensive Monte Carlo simulation study that compares the performances of IF and GSA in identifying differentially expressed gene-sets. The results show that IF outperforms GSA, in most cases significantly, uniformly across all the cases considered. Furthermore, we demonstrate both theoretically and empirically the consistency property of IF, while pointing out the inconsistency of GSA under certain situations. A real data example is considered.
Friday, February 19th, 2010, 3:15pm
Neuberger Hall Room 222
(Refreshments served at 3:00 in Neuberger Hall Room 344)
This event is free and open to the public.