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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
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\centerline{4:15 p.m., Tuesday, January 8, 2002}
\centerline{Sequoia Hall Room 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl Bradley Efron}
\centerline{\sl Department of Statistics}
\centerline{\sl Stanford University}
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\centerline{\bf Least Angle Regression}
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        Automatic model-building algorithms are familiar,
or notorious, in the linear model literature: forward
stepwise, backward stepwise, all subsets, and various
combinations, are used to produce "good" linear models
for predicting a response y on the basis of some measured
quantities x[1],x[2],...,x[p]. Goodness is defined in
terms of prediction accuracy, but also in terms of parsimony:     
simpler models are preferred for the sake of scientific
insight into the x-y relationship.

        The "Lasso" and "Forward Stagewise Linear
Regression" are promising new model-building algorithms
that work differently than the traditional methods.
They give results that are strikingly similar but not
identical to each other, as shown in the examples in
the recent book by Hastie, Tibshirani, and Friedman. It
turns out that both are variates of a simpler technique,
"Least Angle Regression". Besides helping understand the
Lasso/Stagewise methodology, the Least Angle Regression
algorithm is computationally much more efficient. It is
also easier to analyze in terms of its statistical prop-
erties.
                                                           

        Joint work with Trevor Hastie and Rob Tibshirani.         
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