Paper Info
Title
Dimension reduction for the conditional kth moment via central solution space
Abstract
Sufficient dimension reduction aims at finding transformations of predictor X without losing any regression information of Y versus X. If we are only interested in the information contained in the mean function or the kth moment function of Y given X, estimation of the central mean space or the central kth moment space becomes our focus. However, existing estimators for the central mean space and the central kth moment space require a linearity assumption on the predictor distribution. In this paper, we relax this stringent assumption via the notion of central kth moment solution space. Simulation studies and analysis of the Massachusetts college data set confirm that our proposed estimators of the central kth moment space outperform existing methods for non-elliptically distributed predictors.
Year
DOI
Venue
2012
10.1016/j.jmva.2012.06.001
J. Multivariate Analysis
Keywords
Field
DocType
kth moment function,central solution space,predictor x,central kth moment space,dimension reduction,central kth moment solution,mean function,central mean space,regression information,stringent assumption,predictor distribution,conditional kth moment,linearity assumption
Dimensionality reduction,Regression,Standardized moment,Linearity,Moment (mathematics),Statistics,Sufficient dimension reduction,Mathematics,Estimator
Journal
Volume
ISSN
Citations 
112,
0047-259X
0
PageRank 
References 
Authors
0.34
3
2
Name
Order
Citations
PageRank
Yuexiao Dong134.67
Zhou Yu273.08