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  \textbf{\Large{\textsc{STANFORD UNIVERSITY}}}\\[5pt]
  \textbf{\Large{\textsc{DEPARTMENT OF STATISTICS}}}\\[5pt]
  \Large{\textsc{CELEBRATING BRADLEY EFRON'S 70TH BIRTHDAY}}
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  3:00-4:00pm, Tuesday, May 13, 2008\\
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  Packard 101\\
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  \subsection*{Multi-level inference, since Stein}
  \textsl{3:00pm, Carl Morris}\\
  Professor of Statistics\\
  Harvard University
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\paragraph{Abstract}
We review the early and continuing development of shrinkage
estimation, spurred on by applications that demand an ever-widening
range of models and inferences.  Stein's superharmonic measure coupled
with an adjustment for density maximization provides simple and
accurate confidence intervals for random effects.

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\subsection*{Least Angle Regression}
\textsl{3:30pm, Trevor Hastie}\\
Professor of Statistics\\
Stanford University\\
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\paragraph{Abstract}

Least Angle Regression is a new model selection algorithm; a
``democratic'' version of forward stepwise regression.  Inspired by
boosting, it provides an incremental gradient descent path toward the
least-squares fit. LAR has a number of attractive features:
\begin{enumerate}
\item Its computational cost is that of a single full least-squares
    fit.
\item A minor modification of the LAR algorithm gives the
    entire lasso regularization path.
\item A simple formula
    characterizes the degrees of freedom along the path.
\end{enumerate}

This talk will review LAR, and summarize the vast array of research
that it has inspired.


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