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Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR)
Author(s) -
Mevik BjørnHelge,
Cederkvist Henrik René
Publication year - 2004
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.887
Subject(s) - partial least squares regression , principal component regression , statistics , cross validation , estimator , mean squared error , ordinary least squares , principal component analysis , mathematics , regression , standard error , regression analysis , generalized least squares
This paper presents results from simulations based on real data, comparing several competing mean squared error of prediction (MSEP) estimators on principal component regression (PCR) and partial least squares regression (PLSR): leave‐one‐out cross‐validation, K ‐fold and adjusted K ‐fold cross‐validation, the ordinary bootstrap estimate, the bootstrap smoothed cross‐validation (BCV) estimate and the 0.632 bootstrap estimate. The overall performance of the estimators is compared in terms of their bias, variance and squared error. The results indicate that the 0.632 estimate and leave‐one‐out cross‐validation are preferable when one can afford the computation. Otherwise adjusted 5‐ or 10‐fold cross‐validation are good candidates because of their computational efficiency. Copyright © 2005 John Wiley & Sons, Ltd.