Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Регрессия методом обыкновенных наименьших квадратов (ОНМК)× | S-оценка для робастной регрессии× | |
|---|---|---|
| Область≠ | Эконометрика | Статистика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2019 | 1984 |
| Автор метода≠ | Wooldridge (textbook treatment); classical least squares | Rousseeuw & Yohai (1984) |
| Тип≠ | Linear regression | Robust linear regression |
| Основополагающий источник≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Rousseeuw, P. J. & Yohai, V. J. (1984). Robust Regression by Means of S-Estimators. In Robust and Nonlinear Time Series Analysis (Lecture Notes in Statistics, Vol. 26, pp. 256-272). Springer. DOI ↗ |
| Другие названия≠ | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | S-estimation, robust S-regression, S-Tahmin Edici |
| Связанные | 5 | 5 |
| Сводка≠ | 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). | The S-estimator is a robust linear-regression method, introduced by Rousseeuw and Yohai in 1984, that estimates the coefficients by minimising a robust M-estimate of the residual scale rather than the variance of the residuals. By driving down a bounded measure of residual spread it can attain a breakdown point of up to 50%, so it stays reliable even when a large share of the data are outliers, and it provides the first stage of the well-known MM-estimator. |
| ScholarGateНабор данных ↗ |
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