مقایسهٔ روش‌ها

	رگرسیون لجستیک مقاوم	رگرسیون حداقل مربعات معمولی (OLS)
حوزه	آمار	اقتصادسنجی
خانواده	Regression model	Regression model
سال پیدایش	2001	2019
پدیدآور	Cantoni & Ronchetti (2001); Bondell (2008)	Wooldridge (textbook treatment); classical least squares
نوع	Robust generalized linear model (binary outcome)	Linear regression
منبع بنیادین	Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
نام‌های دیگر	robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
مرتبط	5	5
خلاصه	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).	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).