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  \textbf{\Large{\textsc{STANFORD UNIVERSITY}}}\\[5pt]
  \textbf{\Large{\textsc{DEPARTMENT OF STATISTICS}}}\\[5pt]
  \Large{\textsc{DEPARTMENTAL SEMINAR}}
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  4:15pm, Tuesday, April 24, 2007\\
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  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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  \textsl{Ji Zhu} \\
  Department of Statistics\\
  University of Michigan
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  \subsection*{Group Variable Selection via Hierarchical Lasso and Its Oracle Property}
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\noindent

In many engineering and scientific applications, predictor variables are 
grouped, for example, in biological applications where assayed genes or 
proteins can be grouped by biological role.  Common statistical analysis 
methods such as ANOVA, factor analysis, and functional modeling with 
partially ordered basis sets also exhibit natural variable groupings.  We 
develop a new variable selection method that respects the group 
constraint.  Our new method enjoys benefits that existing successful 
methods do not have, while offering the potential for achieving 
theoretical ``oracle'' properties.

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