Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão Linear Robusta× | Regressão Quantílica× | |
|---|---|---|
| Área≠ | Aprendizado de máquina | Econometria |
| Família≠ | Machine learning | Regression model |
| Ano de origem≠ | 1964–1987 | 1978 |
| Autor original≠ | Huber, P. J.; Rousseeuw, P. J. | Koenker & Bassett |
| Tipo≠ | Outlier-resistant supervised regression | Conditional quantile regression |
| Fonte seminal≠ | Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Outros nomes≠ | robust regression, M-estimator regression, Huber regression, outlier-resistant regression | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Relacionados | 5 | 5 |
| Resumo≠ | Robust linear regression fits a linear model between predictors and a continuous outcome while down-weighting or discarding influential outliers, preventing the few anomalous observations that OLS is famously sensitive to from distorting the entire estimated line. Major variants include Huber regression, iteratively reweighted least squares (IRLS), RANSAC, and Theil-Sen estimation. | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
| ScholarGateConjunto de dados ↗ |
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