قارن الطرق

	الانحدار اللوجستي	تقدير MM للانحدار القوي
المجال	إحصاء البحث	الإحصاء
العائلة	Process / pipeline	Regression model
سنة النشأة	1958	1987
صاحب الطريقة	David Roxbee Cox	Victor J. Yohai
النوع	Method	Robust linear regression
المصدر التأسيسي	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.
الأسماء البديلة	logit model, binomial logistic regression, LR	MM-estimation, MM robust regression, high-breakdown high-efficiency estimator, MM-Tahmin Edici
ذات صلة	3	5
الملخص	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.