Principal Component Analisys onto subspace reference in case of multicollinearity
Abstract
En
The Principal Component Analysis onto References Subspaces is a multivariate method to analyze two sets of quantitative variables when between the two sets exists a directional relationship. When the explicative variables are affected by multicollinearity this technique is not recommended. In literature exist many methods to resolve this problem (Ridge Regression, Principal Component Regression, Partial Least Square, Latent Root Regression), this work shows an alternative method based on simple linear regression.
The Principal Component Analysis onto References Subspaces is a multivariate method to analyze two sets of quantitative variables when between the two sets exists a directional relationship. When the explicative variables are affected by multicollinearity this technique is not recommended. In literature exist many methods to resolve this problem (Ridge Regression, Principal Component Regression, Partial Least Square, Latent Root Regression), this work shows an alternative method based on simple linear regression.
DOI Code:
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Keywords:
PCA; PCAR; CPR; Linear Regression
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