Linganisha mbinu

	Usawa wa Takwimu wa Usawazishaji wa Logisti	Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)
Nyanja	Takwimu	Ekonometriki
Familia	Regression model	Regression model
Mwaka wa asili	2001	2019
Mwanzilishi	Cantoni & Ronchetti (2001); Bondell (2008)	Wooldridge (textbook treatment); classical least squares
Aina	Robust generalized linear model (binary outcome)	Linear regression
Chanzo asilia	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
Majina mbadala	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
Zinazohusiana	5	5
Muhtasari	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).