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Principal component regression
Known as:
PCR (disambiguation)
In statistics, principal component regression (PCR) is a regression analysis technique that is based on principal component analysis (PCA). Typically…
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Related topics
Related topics
15 relations
Constrained optimization
Cross-validation (statistics)
Dimensionality reduction
Early stopping
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Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
2011
2011
1 Principal Component Regression as a Countermeasure against Collinearity
Chong Ho Alex Yu
2011
Corpus ID: 153316870
There are different approaches to counteract the threat of multicollinearity in regression modeling, such as centered-score…
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2011
2011
EEG-Based Motion Sickness Estimation Using Principal Component Regression
Li-Wei Ko
,
Chun-Shu Wei
,
Shi-An Chen
,
Chin-Teng Lin
International Conference on Neural Information…
2011
Corpus ID: 36046708
Driver's cognitive state monitoring system has been implicated as a causal factor for the safety driving issue, especially when…
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2011
2011
Nonlinear Robust Regression Using Kernel Principal Component Analysis and R-Estimators
Antoni Wibowo
,
M. I. Desa
2011
Corpus ID: 16299268
In recent years, many algorithms based on kernel principal component analysis (KPCA) have been proposed including kernel…
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2009
2009
Application of Principal Component Regression to Dam Safety Monitoring
Chengfa Deng
2009
Corpus ID: 112768014
Principal component regression is introduced,and displacement prediction of a dam is analyzed.Multi-collinearlity is diagnosed…
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2009
2009
A Monte Carlo Comparison between Ridge and Principal Components Regression Methods
Yazid M. Al-Hassan
2009
Corpus ID: 55132739
A basic assumption concerned with general linear regression model is that there is no correlation (or no multicollinearity…
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2001
2001
Kernel Principal Component Regression with EM Approach to Nonlinear Principal Components Extraction
R. Rosipal
,
L. Trejo
,
A. Cichocki
2001
Corpus ID: 15734725
In kernel based methods such as Support Vector Machines, Kernel PCA, Gaussian Processes or Regularization Networks the…
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1999
1999
Dynamic system multivariate calibration for optimal primary output estimation
R. Ergon
1999
Corpus ID: 123809952
Avhandling (dr.ing.) - Hogskolen i Telemark / Norges teknisk-naturvitenskapelige universitet
1997
1997
Numerical Investigations in Choosing the Number of Principal Components in Principal Component Regression - CASE I
Jae-Kyoung Shin
,
S. Moon
1997
Corpus ID: 116998958
A method is proposed for the choice of the number of principal components in principal component regression based on the…
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1996
1996
Efficient cross-validation of principal components applied to principal component regression
B. Mertens
,
T. Fearn
,
Michael Thompson
Statistics and computing
1996
Corpus ID: 30178619
The cross-validation of principal components is a problem that occurs in many applications of statistics. The naive approach of…
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1979
1979
Using simulation to measure bias in principal components regression
Phil Enns
Online World Conference on Soft Computing in…
1979
Corpus ID: 12029260
Multicollinearity is a serious problem in regression analysis. High correlation among predictor variables can lead to unstable…
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