Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Робастная логистическая регрессия× | Регрессия методом обыкновенных наименьших квадратов (ОНМК)× | |
|---|---|---|
| Область≠ | Статистика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2001 | 2019 |
| Автор метода≠ | Cantoni & Ronchetti (2001); Bondell (2008) | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Robust generalized linear model (binary outcome) | Linear regression |
| Основополагающий источник≠ | Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Другие названия | 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 |
| Связанные | 5 | 5 |
| Сводка≠ | 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). |
| ScholarGateНабор данных ↗ |
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