Zhang, Y;
Fearn, T;
(2015)
A linearization method for partial least squares regression prediction uncertainty.
Chemometrics and Intelligent Laboratory Systems
, 140
10.1016/j.chemolab.2014.11.011.
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Abstract
We study a local linearization approach put forward by Romera to provide an approximate variance for predictions in partial least squares regression. We note and correct some problems with the original formulae, study the stability of the resulting approximation using some simulations, and suggest an alternative method of computation using a parametric bootstrap. The alternative method is more stable than the algebraic approximation and is faster when the number of predictors is large.
| Type: | Article |
|---|---|
| Title: | A linearization method for partial least squares regression prediction uncertainty |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.chemolab.2014.11.011 |
| Publisher version: | http://dx.doi.org/10.1016/j.chemolab.2014.11.011 |
| Language: | English |
| Additional information: | © 2014. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ Final text available at: http://dx.doi.org/10.1016/j.chemolab.2014.11.011 |
| Keywords: | Multivariate calibration, partial least squares regression, mean squared prediction error, linearization, parametric bootstrap |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1456660 |
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