Сравнение методов

	Робастная логистическая регрессия	MM-оценка для робастной регрессии	Квантильная регрессия
Область	Статистика	Статистика	Эконометрика
Семейство	Regression model	Regression model	Regression model
Год появления	2001	1987	1978
Автор метода	Cantoni & Ronchetti (2001); Bondell (2008)	Victor J. Yohai	Koenker & Bassett
Тип	Robust generalized linear model (binary outcome)	Robust linear regression	Conditional quantile regression
Основополагающий источник	Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030.	Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Другие названия	robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici	conditional quantile regression, regression quantiles, Kantil Regresyon
Связанные	5	5	5
Сводка	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).	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.