Variable Selection and Regularization in Quantile Regression via Minimum Covariance Determinant Based Weights

The importance of variable selection and regularization procedures in multiple regression analysis cannot be overemphasized. These procedures are adversely affected by predictor space data aberrations as well as outliers in the response space. To counter the latter, robust statistical procedures suc...

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Bibliografische gegevens

Hoofdauteurs:	Edmore Ranganai, Innocent Mudhombo
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	MDPI AG 2020-12-01
Reeks:	Entropy
Onderwerpen:	weighted quantile regression RIDGE penalty LASSO penalty elastic net penalty high leverage points collinearity influential points
Online toegang:	https://www.mdpi.com/1099-4300/23/1/33
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