The determination of a "least quantile of squares regression line" for all quantiles
Least median of squares regression has shown to be an extremely useful tool in robust regression analysis. In this note, we extend this concept to least quantile of squares regression, and propose a polynomial algorithm that finds simultaneously an estimator for each quantile. This leads to a propos...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 1994 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/107826 |
| Acceso en línea: | https://hdl.handle.net/11441/107826 https://doi.org/10.1016/0167-9473(94)00059-R |
| Access Level: | acceso abierto |
| Palabra clave: | Least median of squares regression Robust regression Sweep-line technique Minquantile optimization |
| Sumario: | Least median of squares regression has shown to be an extremely useful tool in robust regression analysis. In this note, we extend this concept to least quantile of squares regression, and propose a polynomial algorithm that finds simultaneously an estimator for each quantile. This leads to a proposal of a robust minimum scale regression line and a polynomial algorithm for its determination. |
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