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Statistics Seminar Series
Wednesday, Feb. 13, 2013, 2:30 PM
Cullimore, Room 611
New Jersey Institute of Technology
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Identify
Interactions for High Dimensional Data
Ning Hao
Department of Mathematics, University of Arizona
Abstract
Contemporary statistical
research is being motivated and reshaped by the big data produced from emerging
technologies and innovations in
science and engineering. The high dimensionality, which characterizes many modern data sets, is one of the
main challenges for statisticians
in the new century. In spite of the rapid development in high dimensional statistical learning, it has not been
touched until recently that the
problem of interaction selection for high dimensional data. In this talk, I will introduce a new class of
methodologies to solve regression models with interactions.
The new methods are featured with
feasible implementation, fast speed in computation, and desired theoretical properties. Various examples are
presented to illustrate the new proposals.