Mean squared error of prediction (MSEP) estimates for principal component regression (PCR) and partial least squares regression (PLSR)

By Bjørn-Helge Mevik, H. R. Cederkvist. 2004

252 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/98b06e45b32940e0892db9b7361a1f3db2e16eb7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Ron Wehrens
2. Scott Anthony Sisson
3. Dennis Prangle
4. Matthew A. Nunes
5. Michael G. B. Blum
6. M. Febrero–Bande
7. Bjørn-Helge Mevik

In Statistics and Probability

1. Dennis Prangle
2. Scott Anthony Sisson
3. Michael G. B. Blum
4. Matthew A. Nunes
5. Ron Wehrens
6. M. Febrero–Bande
7. Sadi Alawadi
8. Senén Barro

Citations in the corpus

• An extensive experimental survey of regression methods (2019, methodology): “, using the prin774 cipal components as inputs [41].”
• A comparative review of dimension reduction methods in approximate Bayesian computation (2012, methodology): “These methods make use of a regression layer within the ABC framework, whereby the response variable, θ, is regressed by the (possibly transformed) predictor variables, s, (Beaumont et al. 2002; Blum and François 2010).”
• The pls Package: Principal Component and Partial Least Squares Regression in R (2007, methodology): “There are two cross-validation estimates: CV is the ordinary CV estimate, and adjCV is a bias-corrected CV estimate (Mevik and Cederkvist 2004) (For a LOO CV, there is virtually no difference).”