So sánh phương pháp

	Hồi quy Logistic Mạnh mẽ	Hồi quy Logistic	Ước lượng MM cho hồi quy vững mạnh
Lĩnh vực	Thống kê	Thống kê nghiên cứu	Thống kê
Họ	Regression model	Process / pipeline	Regression model
Năm ra đời	2001	1958	1987
Người khởi xướng	Cantoni & Ronchetti (2001); Bondell (2008)	David Roxbee Cox	Victor J. Yohai
Loại	Robust generalized linear model (binary outcome)	Method	Robust linear regression
Công trình gốc	Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15(2), 642-656.
Tên gọi khác	robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon	logit model, binomial logistic regression, LR	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici
Liên quan	5	3	5
Tóm tắt	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).	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	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.