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Penalized regression on principal manifolds with application to combustion modelling

Einbeck, J.; Isaac, B.; Evers, L.; Parente, A.

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Authors

B. Isaac

L. Evers

A. Parente



Contributors

A. Komarek
Editor

Abstract

For multivariate regression problems featuring strong and non–linear dependency patterns between the involved predictors, it is attractive to reduce the dimension of the estimation problem by approximating the predictor space through a principal surface (or manifold). In this work, a new approach for non- parametric regression onto the fitted manifold is provided. The proposed penal- ized regression technique is applied onto data from a simulated combustion sys- tem, and is shown, in this application, to compare well with competing regression routines.

Citation

Einbeck, J., Isaac, B., Evers, L., & Parente, A. (2012). Penalized regression on principal manifolds with application to combustion modelling. In A. Komarek, & S. Nagy (Eds.), 27th International Workshop on Statistical Modelling, 16-20 July 2012, Prague, Czech Republic ; proceedings (117-122)

Conference Name International workshop on statistical modelling
Conference Location Prague
Publication Date Jan 1, 2012
Deposit Date May 29, 2013
Publicly Available Date Mar 29, 2024
Volume 1
Pages 117-122
Book Title 27th International Workshop on Statistical Modelling, 16-20 July 2012, Prague, Czech Republic ; proceedings.
Keywords Smoothing, Principal component analysis, Local principal manifolds,
Public URL https://durham-repository.worktribe.com/output/1156424
Publisher URL http://www.statmod.org/workshops_archive_proceedings_2012.htm
Additional Information http://www.maths.dur.ac.uk/~dma0je/Papers/einbeck_etal_iwsm2012.pdf

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