In this article, we describe how the theory of sufficient
dimension reduction, and a well-known inference method for it (sliced inverse
regression), can be extended to regression analyses involving both quantitative
and categorical predictor variables. As statistics faces an increasing need for
effective analysis strategies for high-dimensional data, the results we present
significantly widen the applicative scope of sufficient dimension reduction and
open the way for a new class of theoretical and methodological
developments.
Publié le : 2002-04-14
Classification:
Central subspace,
graphics,
SAVE,
SIR,
visualization,
62G08,
62G09,
62H05
@article{1021379862,
author = {Chiaromonte, Francesca and Cook, R.Dennis and Li, Bing},
title = {Sufficient dimensions reduction in regressions with categorical
predictors},
journal = {Ann. Statist.},
volume = {30},
number = {1},
year = {2002},
pages = { 475-497},
language = {en},
url = {http://dml.mathdoc.fr/item/1021379862}
}
Chiaromonte, Francesca; Cook, R.Dennis; Li, Bing. Sufficient dimensions reduction in regressions with categorical
predictors. Ann. Statist., Tome 30 (2002) no. 1, pp. 475-497. http://gdmltest.u-ga.fr/item/1021379862/