Сравнение на методи

	Устойчива логистична регресия	Логистична регресия	Квантилна регресия
Област	Статистика	Статистика за изследвания	Иконометрия
Семейство	Regression model	Process / pipeline	Regression model
Година на възникване	2001	1958	1978
Създател	Cantoni & Ronchetti (2001); Bondell (2008)	David Roxbee Cox	Koenker & Bassett
Тип	Robust generalized linear model (binary outcome)	Method	Conditional quantile regression
Основополагащ източник	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Други названия	robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon	logit model, binomial logistic regression, LR	conditional quantile regression, regression quantiles, Kantil Regresyon
Свързани	5	3	5
Резюме	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.	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.