2015
DOI: 10.5351/csam.2015.22.2.173
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Abstract: We propose a selection procedure of principal components in principal component regression. Our method selects principal components using variable selection procedures instead of a small subset of major principal components in principal component regression. Our procedure consists of two steps to improve estimation and prediction. First, we reduce the number of principal components using the conventional principal component regression to yield the set of candidate principal components and then select principal… Show more

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Cited by 18 publications
(18 citation statements)
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“…In the case of gene expression data, gene co-expression networks are of interest to us, and we do not necessarily want to select one of a set of co-expressed genes. Therefore, we opted instead to use a LASSO-PCR approach [53, 30]. Such an approach will reduce the dimensions of the data while preserving gene co-expression networks, yet still allow for a sparse selection of features.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…In the case of gene expression data, gene co-expression networks are of interest to us, and we do not necessarily want to select one of a set of co-expressed genes. Therefore, we opted instead to use a LASSO-PCR approach [53, 30]. Such an approach will reduce the dimensions of the data while preserving gene co-expression networks, yet still allow for a sparse selection of features.…”
Section: Methodsmentioning
confidence: 99%
“…There are limitations to this approach. Beginning with the full set of components can incidentally retain small components and make estimates of beta coefficients unstable [30]. Interpretation of the components is challenging, and here they were generated without the dependent variable (the measurements along the anterior-posterior axes).…”
Section: Methodsmentioning
confidence: 99%
“…However, they may also be used for prediction [19,20]. An example of regression algorithms may be similar to each other: multiple linear regression (MLR) [21] and principal component regression (PCR) [22]. They are mainly used in data analysis for finding the relationship among variables that effect the prediction of variable values (e.g., chemical compounds' properties).…”
Section: The Outline Of Chemometric Toolsmentioning
confidence: 99%
“…Other methods range from this method to more specialized techniques for regularization (Naes, and Marten, 1988). The PCR method has been proposed as alternatives to the OLS estimator when the independent assumption has not been satisfied in the analysis (Massy, 1965;Lee et al, 2015).…”
Section: Introductionmentioning
confidence: 99%