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Robustna logistička regresija×Kvantilna regresija×
PodručjeStatistikaEkonometrija
ObiteljRegression modelRegression model
Godina nastanka20011978
TvoracCantoni & Ronchetti (2001); Bondell (2008)Koenker & Bassett
VrstaRobust generalized linear model (binary outcome)Conditional quantile regression
Temeljni izvorCantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030. DOI ↗Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗
Drugi nazivirobust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyonconditional quantile regression, regression quantiles, Kantil Regresyon
Srodne55
SažetakRobust 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).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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ScholarGateUsporedite metode: Robust Logistic Regression · Quantile Regression. Preuzeto 2026-06-17 s https://scholargate.app/hr/compare