The talk deals with the problem of
dimension reduction in the general context of supervised
statistical learning. In particular, data mining applications with
many covariates often lead to high dimensional data analysis
problems. The main goal of the proposed methodology is to improve
tree based methods as prediction tool by introducing an
alternative approach to data partitioning which is meant to handle
large numbers of (correlated) covariates. The key idea is to use
suitable combinations of covariates recursively identified. This modified recursive partitioning
is adopted to investigate the structure of expansions and
recessions on U.S. business cycl
