手法を比較

	ロバストロジスティック回帰	ロジスティック回帰	MM推定によるロバスト回帰
分野	統計学	研究統計	統計学
系統	Regression model	Process / pipeline	Regression model
提唱年	2001	1958	1987
提唱者	Cantoni & Ronchetti (2001); Bondell (2008)	David Roxbee Cox	Victor J. Yohai
種類	Robust generalized linear model (binary outcome)	Method	Robust linear regression
原典	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.
別名	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
関連	5	3	5
概要	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.