SHRINKAGE REGRESSION METHODS APPLIED TO AGRICULTURE
Suna Akkol
Pages: 214-221
Publié: 1 Jun 2014
Vues: 5,240
Téléchargements: 1,128
Citations: 1 (Google Scholar)
Citations: 1 (OpenAlex)
Résumé: In Multiple Linear Regression a common goal is to determine which independent variables contribute significantly to explaining the variability in the dependent variable. Multicollinearity occurs when there is a linear relationship among one or more of the independent variables. It is a statistical term for a problem that is common in technical analysis. When there is multicollinearity among the explanatory variables in the multiple regression model, the properties of BLUE is no longer effective. Shrinkage regression that refers to shrinkage methods of estimation or prediction in multiple linear regression can be used to obtain more robust results of the data than the ordinary least square (OLS). In this study it will be presented Ridge Regression, LASSO and Elestic-net that are shrinkage methods.
Mots-clés : ordinary least square, lasso, ridge regression, elastic-net multicollinearity
Citer cet article : Suna Akkol. SHRINKAGE REGRESSION METHODS APPLIED TO AGRICULTURE. Journal of International Scientific Publications: Agriculture & Food 2, 214-221 (2014). https://www.scientific-publications.net/en/article/1000029/
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