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Robust Weighted Least Squares (Robust WLS)×Regressão Quantílica×
ÁreaEconometriaEconometria
FamíliaRegression modelRegression model
Ano de origem1964/19811978
Autor originalHuber, P. J.Koenker & Bassett
TipoRobust weighted regressionConditional quantile regression
Fonte seminalHuber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗
Outros nomesrobust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regressionconditional quantile regression, regression quantiles, Kantil Regresyon
Relacionados55
ResumoRobust WLS combines weighted least squares — which corrects for known or estimated heteroscedasticity — with robust M-estimation that down-weights influential outliers. The result is a regression estimator that is simultaneously efficient under non-constant error variance and resistant to observations that would otherwise distort coefficient estimates.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.
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ScholarGateComparar métodos: Robust WLS · Quantile Regression. Recuperado em 2026-06-17 de https://scholargate.app/pt/compare