Сравнение методов

	Робастная логистическая регрессия	MM-оценка для робастной регрессии	Регрессия методом обыкновенных наименьших квадратов (ОНМК)	Квантильная регрессия
Область	Статистика	Статистика	Эконометрика	Эконометрика
Семейство	Regression model	Regression model	Regression model	Regression model
Год появления	2001	1987	2019	1978
Автор метода	Cantoni & Ronchetti (2001); Bondell (2008)	Victor J. Yohai	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett
Тип	Robust generalized linear model (binary outcome)	Robust linear regression	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	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	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
Связанные	5	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.