Fitting a linear regression model by combining least squares and least absolute value estimation
Robust estimation of the multiple regression is modeled by using a convex combination of Least Squares and Least Absolute Value criterions. A Bicriterion Parametric algorithm is developed for computing the corresponding estimates. The proposed procedure should be specially useful when outliers are e...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 1995 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/4056 |
| Acceso en línea: | https://hdl.handle.net/2099/4056 |
| Access Level: | acceso abierto |
| Palabra clave: | Inference Outliers in regression L1 regression Bicriterion parametric algorithm Inferència Classificació AMS::62 Statistics::62F Parametric inference Classificació AMS::62 Statistics::62J Linear inference, regression |
| Sumario: | Robust estimation of the multiple regression is modeled by using a convex combination of Least Squares and Least Absolute Value criterions. A Bicriterion Parametric algorithm is developed for computing the corresponding estimates. The proposed procedure should be specially useful when outliers are expected. Its behavior is analyzed using some examples. |
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