Student Seminar Series - July 7, 2006
University of Minnesota
School of Statistics
College of Liberal Arts
Inference
and Model Selection
Yingwen
Dong
Friday, July 7, 2006
10:00 AM, 170
Ford Hall
Minneapolis, East Bank Campus
Refreshments at 9:30 AM
300 Ford Hall
Abstract
Model selection is often employed in many scientific studies and has a
significant effect on inference of estimated parameters. Common
practice is to use a selected model to estimate parameters of interest
ignoring uncertainty introduced by the process of model selection. It
is well known that this could yield overoptimistic inferences,
resulting in misleading conclusions. In this proposal, we propose an
inferential tool given a selected model, together with an estimated
probability of selecting the model, which measures the uncertainty of
model selection in contrast to simply applying the standard inferential
tools to the estimators. The probability can be estimated by using a
data perturbation technique.
We show that the proposed technique yields approximately unbiased
estimation through theoretical analysis and simulations. We also
examine the proposed method in the context of hypothesis testing, and
demonstrate through simulation that it performs well in the situation
when the commonly used method breaks down. The results illustrate that
the proposed method is attractive.