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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Detalles Bibliográficos
Autores principales: Ranganai, Edmore, Mudhombo, Innocent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7823782/
https://www.ncbi.nlm.nih.gov/pubmed/33383623
http://dx.doi.org/10.3390/e23010033

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7823782/
https://www.ncbi.nlm.nih.gov/pubmed/33383623
http://dx.doi.org/10.3390/e23010033

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