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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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  %% Time of talk, Weekday, Date of talk\\
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  4:15 p.m., Tuesday, February 5th, 2008\\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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  \textsl{Peter Radchenko} \\
  USC
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  \subsection*{Variable Inclusion and Shrinkage Algorithms}
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\noindent
The Lasso is a popular and computationally efficient procedure for
automatically performing both variable selection and coefficient
shrinkage on linear regression models.  One limitation of the Lasso is
that the same tuning parameter is used for both variable selection and
shrinkage. As a result, it may end up selecting a model with too many
variables to prevent over shrinkage of the regression coefficients.  We
will discuss a new class of methods called Variable Inclusion and
Shrinkage Algorithms (VISA).  This approach is capable of selecting
sparse models while avoiding over shrinkage problems.  VISA uses a path
algorithm, so it is computationally efficient.  It will be shown through
extensive simulations that the new approach significantly outperforms
the Lasso and also provides improvements over more recent procedures,
such as the Dantzig selector, Relaxed Lasso and Adaptive Lasso.

We will also discuss a new algorithm, DASSO, for fitting the entire
coefficient path of the Dantzig selector.  A slight simplification of
DASSO produces LARS, which is typically used to construct the Lasso
path.
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