Two nonparametric methods for multiple regression transform selection
are provided. The first, Alternative Conditional Expectations (ACE),
is an algorithm to find the fixed point of maximal correlation, i.e.
it finds a set of transformed response variables that maximizes R^2
using smoothing functions [see Breiman, L., and J.H. Friedman. 1985.
"Estimating Optimal Transformations for Multiple Regression and
Correlation". Journal of the American Statistical Association.
80:580-598. ]. Also included is
the Additivity Variance Stabilization (AVAS) method which works better
than ACE when correlation is low [see Tibshirani, R.. 1986.
"Estimating Transformations for Regression via Additivity and Variance
Stabilization". Journal of the American Statistical Association.
83:394-405. ]. A good introduction
to these two methods is in chapter 16 of Frank Harrel's "Regression
Modeling Strategies" in the Springer Series in Statistics.