Abstract
In this paper we present a new stepwise method for selecting predictor variables in linear regression models and its application to agricultural data analysis. This method is an extension of principal component regression, and it consists of iteratively selecting original predictor variables one at a time from repeatedly selected subsets of principal components. The reasoning behind the method and its implementation are discussed, and an example of applying the method to agricultural data is given. The example also demonstrates the advantages of the proposed method over some known methods.
Keywords
Variable selection, principal components, multicollinearity
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Boneh, Shahar and Mendieta, Gonzalo R.
(1992).
"REGRESSION MODELING USING PRINCIPAL COMPONENTS,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1408
REGRESSION MODELING USING PRINCIPAL COMPONENTS
In this paper we present a new stepwise method for selecting predictor variables in linear regression models and its application to agricultural data analysis. This method is an extension of principal component regression, and it consists of iteratively selecting original predictor variables one at a time from repeatedly selected subsets of principal components. The reasoning behind the method and its implementation are discussed, and an example of applying the method to agricultural data is given. The example also demonstrates the advantages of the proposed method over some known methods.
