Professor Jochen Einbeck jochen.einbeck@durham.ac.uk
Professor
Penalized regression on principal manifolds with application to combustion modelling
Einbeck, J.; Isaac, B.; Evers, L.; Parente, A.
Authors
B. Isaac
L. Evers
A. Parente
Contributors
A. Komarek
Editor
Dr Silvia Nagy silvia.nagy@durham.ac.uk
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 |
Files
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