On Partial Sufficient Dimension Reduction with Applications to Partially Linear Multi-Index Models
Partial dimension reduction is a general method to seek informative convex combinations of predictors of primary interest, which includes dimension reduction as its special case when the predictors in the remaining part are constants. In this article, we propose a novel method to conduct partial dimension reduction estimation for predictors of primary interest without assuming that the remaining predictors are categorical. To this end, we first take the dichotomization step such that any existing approach for partial dimension reduction estimation can be employed. Then we take the expectation step to integrate over all the dichotomic predictors to identify the partial central subspace. As an example, we use the partially linear multi-index model to illustrate its applications for semiparametric modeling. Simulations and real data examples are given to illustrate our methodology.
Z. Feng et al., "On Partial Sufficient Dimension Reduction with Applications to Partially Linear Multi-Index Models," Journal of the American Statistical Association, Taylor & Francis, Jan 2013.
The definitive version is available at http://dx.doi.org/10.1080/01621459.2012.746065
Mathematics and Statistics
Keywords and Phrases
partial central subspace; partial discretization-expectation estimation; partially linear model
Article - Journal
© 2013 Taylor & Francis, All rights reserved.