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Bootstrapping principal component regression models

โœ Scribed by R. Wehrens; W. E. van der Linden

Publisher
John Wiley and Sons
Year
1997
Tongue
English
Weight
325 KB
Volume
11
Category
Article
ISSN
0886-9383

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โœฆ Synopsis

Bootstrap methods can be used as an alternative for cross-validation in regression procedures such as principal component regression (PCR). Several bootstrap methods for the estimation of prediction errors and confidence intervals are presented. It is shown that bootstrap error estimates are consistent with cross-validation estimates but exhibit less variability. This makes it easier to select the correct number of latent variables in the model. Using bootstrap confidence intervals for the regression vectors, it is possible to select a subset of the original variables to include in the regression, yielding a more parsimonious model with smaller prediction errors. The methods are illustrated using PCR, but can be applied to all regression models yielding a vector or matrix of regression coefficients.

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โœ E. Vigneau; M. F. Devaux; E. M. Qannari; P. Robert ๐Ÿ“‚ Article ๐Ÿ“… 1997 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 160 KB ๐Ÿ‘ 2 views

Ridge regression (RR) and principal component regression (PCR) are two popular methods intended to overcome the problem of multicollinearity which arises with spectral data. The present study compares the performances of RR and PCR in addition to ordinary least squares (OLS) and partial least square