Localized regression on principal manifolds

Einbeck, Jochen; Evers, Ludger

Abstract

We consider nonparametric dimension reduction techniques for multivariate regression problems in which the variables constituting the predictor space are strongly nonlinearly related. Specifically, the predictor space is approximated via ``local'' principal manifolds, based on which a kernel regression is carried out.

Citation

Einbeck, J., & Evers, L. (2010, July). Localized regression on principal manifolds. Presented at 25th International Workshop on Statistical Modelling., Glasgow

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 25th International Workshop on Statistical Modelling.
• Start Date: Jul 5, 2010
• End Date: Jul 9, 2010
• Publication Date: Jul 1, 2010
• Deposit Date: Jan 13, 2011
• Publicly Available Date: Oct 25, 2011
• Publisher: University of Glasgow
• Pages: 179-184
• Keywords: Smoothing, Principal curves and surfaces, Localized PCA.
• Public URL: https://durham-repository.worktribe.com/output/1159171
• Publisher URL: http://www.statmod.org/