Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| S-оценка за робастна регресия× | Метод на най-малките квадрати (МНК)× | |
|---|---|---|
| Област≠ | Статистика | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1984 | 2019 |
| Създател≠ | Rousseeuw & Yohai (1984) | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Robust linear regression | Linear regression |
| Основополагащ източник≠ | 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 ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Други названия≠ | S-estimation, robust S-regression, S-Tahmin Edici | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Свързани | 5 | 5 |
| Резюме≠ | 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. | 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 от данни ↗ |
|
|