Σύγκριση μεθόδων

	Εύρωστη Λογιστική Παλινδρόμηση	Παλινδρόμηση Ποσοστημορίων
Πεδίο	Στατιστική	Οικονομετρία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	2001	1978
Δημιουργός	Cantoni & Ronchetti (2001); Bondell (2008)	Koenker & Bassett
Τύπος	Robust generalized linear model (binary outcome)	Conditional quantile regression
Θεμελιώδης πηγή	Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Εναλλακτικές ονομασίες	robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon	conditional quantile regression, regression quantiles, Kantil Regresyon
Συναφείς	5	5
Σύνοψη	Robust 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.