San Francisco Bay Area Chapter of American Statistical
Association
Monthly Seminar
Time: |
4:30 – 6:00 pm, April 17,
2017 (4:30-5:00 networking and light
refreshment, 5:00-6:00 seminar) |
Location: |
Building 303 Bay view
conference room, Gilead 303 Velocity, Foster City, CA 94404 *Free parking* Please check in at the
front desk. |
Speaker: |
Dr. Tao He Assistant Professor Department of Mathematics, San Francisco State University Email: hetao@sfsu.edu |
Title: |
Testing High-dimensional Non-parametric Functions with
Application in Gene Set Analysis |
Abstract:
High-dimensional data arise nowadays in a wide
range of areas, such as biology, imaging and climate. A common feature of
high-dimensional data is that the number of features could be much larger than
the sample size, the so-called “large p, small n” problem. A specific example
in genomic studies is encountered when detecting the significant gene sets that
are associated with certain trait. To model the systematic mechanism and
potential complex interactions among genetic variants, we consider a flexible
nonparametric function in a reproducing kernel Hilbert space. A test statistic
is then proposed and its asymptotic distributions are studied under the null
hypothesis and a series of local alternative hypotheses, under the “large p,
small n” setting. The methods were demonstrated through extensive simulation
studies and real data analysis.
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