Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR)
Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR)
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DOI：
10.1002/cem.887
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发表时间：
2004-09-01
影响因子：
2.4
通讯作者：
Cederkvist, HR
中科院分区：
文献类型：
--
作者：
Mevik, BH;Cederkvist, HR
本文给出了基于真实的数据的模拟结果，比较了主成分回归（PCR）和偏最小二乘回归（PLSR）的几种相互竞争的预测均方误差（MSEP）估计：留一交叉验证、K折和调整K折交叉验证、普通Bootstrap估计、Bootstrap平滑交叉验证（BCV）估计和0.632 Bootstrap估计。估计的整体性能进行比较，在他们的偏见，方差和平方，误差。结果表明，在计算量允许的情况下，0.632估计和留一法交叉验证是比较可取的。另外，调整的5或10折交叉验证是很好的候选者，因为它们的计算效率。版权所有（c）2005约翰威利T儿子，有限公司.
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 (c) 2005 John Wiley T Sons, Ltd.