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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 optimizationCross-validation (statistics)Dimensionality reductionEarly stopping

Papers overview

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2011
2011
1 Principal Component Regression as a Countermeasure against Collinearity
There are different approaches to counteract the threat of multicollinearity in regression modeling, such as centered-score… 
2010
2010
Additive Risk Analysis of microarray Gene Expression Data via Correlation Principal Component Regression
In order to predict future patients' survival time based on their microarray gene expression data, one interesting question is… 
2010
2010
Sparse Principal Component Regression
Modeling of complex systems is usually confronted with high dimensional independent variables. Econometric models are usually… 
2009
2009
Application of Principal Component Regression to Dam Safety Monitoring
Principal component regression is introduced,and displacement prediction of a dam is analyzed.Multi-collinearlity is diagnosed… 
2005
2005
Genetic algorithms for Hyperspectral Range and Operator Selection
A novel genetic algorithm was developed using mathematical operations on spectral ranges to explore spectral operator space and… 
2001
2001
Kernel Principal Component Regression with EM Approach to Nonlinear Principal Components Extraction
In kernel based methods such as Support Vector Machines, Kernel PCA, Gaussian Processes or Regularization Networks the… 
1999
1999
Dynamic system multivariate calibration for optimal primary output estimation
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
A method is proposed for the choice of the number of principal components in principal component regression based on the… 
1996
1996
Efficient cross-validation of principal components applied to principal component regression
The cross-validation of principal components is a problem that occurs in many applications of statistics. The naive approach of… 
1979
1979
Using simulation to measure bias in principal components regression
Multicollinearity is a serious problem in regression analysis. High correlation among predictor variables can lead to unstable… 
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