Department of
presents
Dr.
Huixia (Judy) Wang,
Dr. Donald Martin, &
Dr. Lexin Li
A Sampler of NCSU Faculty
Quantile
Rank Score Test for Linear Models with a Random Effect
Abstract: In this talk, I will introduce a
rank score test for linear quantile
regression models with a random effect. The developed test makes no
distributional assumptions, and it does not require estimating the unknown
error density function. The test is shown to be a very valuable
complement to the usual mixed model analysis based on Gaussian likelihood.
I will point out two applications of the proposed test. One
is in GeneChip microarray studies for detecting
differentially expressed genes through analyzing probe level measurements. An
enhanced method is proposed to improve the test efficiency at small samples by
sharing information across the "interesting" genes. Another
application is on analyzing biomedical data where measurements are censored due
to a fixed quantification limit.
Distributions of patterns and statistics in
random sequences
Abstract: A unified methodology for computing
distributions of patterns and statistics in random sequences is discussed.
An auxiliary Markov chain is developed such that events in the original
sequence correspond to the auxiliary chain lying in a class of states.
Once this association is made, probabilities are computed through tracking the
movements of the auxiliary chain through its states. We highlight the variety
of distributions that may be computed in this manner, including those for
patterns and statistics in "observation" sequences, and also in
hidden "state" sequences modeled by probabilistic graphical models
such as hidden Markov models, with a focus on interesting applications.
Both past research and future research directions
are given.
Sufficient
Dimension Reduction with Application to Microarray
Data Analysis
Abstract: Sufficient dimension reduction can
aid the analysis of high-dimensional microarray
data by transforming the problems to low dimensional projections. The curse of
dimensionality is often alleviated, and the informative data visualization may
be enabled. In this talk, we
start
with an application of a dimension reduction method, sliced inverse regression,
to a microarray survival
data analysis. This exercise also introduces new challenges to the methodology
of sufficient dimension reduction, including the presence of highly correlated
predictors, the small-n-large-p problem, and variable selection in the
framework of dimension reduction. We next continue the talk with a discussion
of some recently proposed regularized dimension reduction methods to address
the above challenges. Some theoretical properties of the proposed methods
will be explored, and the analysis of the microarray data will underlie this line of
methodology development.
Friday, September 14, 2007
3:35 - 4:35 pm
301 Riddick Hall
Refreshments will be served in the
common area of 301 Riddick at 3:00 pm.