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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENT SEMINAR}
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\centerline{4:15 p.m., Tuesday, January 29, 2002}
\centerline{Sequoia Hall Room 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl Hugh Chipman}
\centerline{\sl University of Waterloo}
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\centerline{\bf Treed Generalized Linear Models}
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Tree models can be an effective and interpretable tool for supervised
learning problems (i.e., regression and classification).  A recent
variation on trees is the "treed model", which includes a more
sophisticated model in each terminal node of the tree, such as a linear
regression.  This talk considers generalized linear models as a broader
class of terminal node models.  Specific examples include binary and
Poisson regression.  A Bayesian approach to this problem offers several
advantages, including regularization through careful specification of
prior distributions, a stochastic search in the tree space, and the
potential to improve predictions by model averaging.  Data mining
applications in areas such as marketing, insurance, and drug discovery
will be discussed.


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