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Colloquium Series, Department of Statistics, University of Toronto

Topic:     Multivariate Adaptive Regression Splines
Speaker:   Jerome H. Friedman, Department of Statistics and Stanford
           Linear Accelerator Center, Stanford University
Date:      Friday, March 31, 1989    10:00 a.m.
Place:     Room 1074, Sidney Smith Hall, 100 St. George St., U of T
Abstract:

A new method is presented for flexible regression modeling of high dimensional
data.  The model takes the form of an expansion in product spline basis 
functions where the number of basis functions as well as the parameters 
associated with each one (product degree and knot locations) are automatically
determined by the data.  This procedure is motivated by the recursive 
partitioning approach to regression and shares its attractive properties.
Unlike recursive partitioning, however, this method produces continuous
models with continuous derivatives.  It has more power and flexibility to 
model relationships that are nearly additive or involve interactions in at 
most a few variables.  In addition, the model can be represented in a form 
that identifies separately the additive contribution of each variable, as well
as the individual contributions associated with the different multivariable
interactions.