Statistics Colloquium
November 1 (Fri) 11 a.m.
Alavi Commons Room, 6625 Everett Tower
Variable selection based on relevant information
Dr. Tanujit Dey
Department of Mathematics
College of William And Mary
In synchrony with the current advancement of science and technology,
more complex and multi-dimensional data becomes ubiquitous.
This status quo is both challenge and incentive for considering
variable selection problems in generally ill-defined situations
(number of variables in the data exceeding the number of observations).
In this research, by assuming the framework of a linear model,
our principle goal is to identify the relationship between
certain outcomes and a few variables from the data set.
Considering high to ultra-high dimensional cases,
most variables are believed not responsible for expressing the
outcome. Hence the objective would be to abandon those irrelevant
variables from the data and hold on to only those that are more relevant
for expressing the outcome. In order to do so, we propose a novel variable
selection procedure along with supportive theoretical results.
Extensive simulation studies have been performed to illustrate the
performance of the proposed methodology in comparison with other
familiar methods from the specialty literature. A number of real data
sets have been analyzed from the point of view of the predictive
performance to defend the novelty of the proposed methodology.
All statistics students are expected to attend.
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Past colloquiums
