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Robust logistisk regression×MM-estimering för robust regression×Kvantilregression×
ÄmnesområdeStatistikStatistikEkonometri
FamiljRegression modelRegression modelRegression model
Ursprungsår200119871978
UpphovspersonCantoni & Ronchetti (2001); Bondell (2008)Victor J. YohaiKoenker & Bassett
TypRobust generalized linear model (binary outcome)Robust linear regressionConditional quantile regression
UrsprungskällaCantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030. DOI ↗Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656. DOI ↗Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗
Aliasrobust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik RegresyonMM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Ediciconditional quantile regression, regression quantiles, Kantil Regresyon
Närliggande555
SammanfattningRobust Logistic Regression is a variant of logistic regression that is resistant to outliers and leverage points, fitting a binary or categorical outcome with Mallows-type weighted estimation. The robust framework for generalized linear models was developed by Cantoni and Ronchetti (2001), with a weighting approach later refined by Bondell (2008).The MM-estimator is a robust linear regression method introduced by Victor J. Yohai in 1987. It combines the high breakdown point of an S-estimator with the high efficiency of an M-estimator, so it resists outliers strongly while still using the data efficiently when errors are well-behaved.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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ScholarGateJämför metoder: Robust Logistic Regression · MM-Estimator · Quantile Regression. Hämtad 2026-06-19 från https://scholargate.app/sv/compare