Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Оценител на Theil-Sen× | Метод на най-малките квадрати (МНК)× | |
|---|---|---|
| Област≠ | Статистика | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1968 | 2019 |
| Създател≠ | Henri Theil (1950); P. K. Sen (1968) | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Robust linear regression | Linear regression |
| Основополагащ източник≠ | Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association, 63(324), 1379-1389. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Други названия≠ | Theil-Sen Tahmincisi, Theil-Sen regression, median slope estimator, Sen's slope estimator | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Свързани≠ | 6 | 5 |
| Резюме≠ | The Theil-Sen estimator is a robust linear regression method that estimates the slope as the median of the slopes computed over all pairs of data points. Introduced by Henri Theil in 1950 and extended by P. K. Sen in 1968, it tolerates outliers in the response with a breakdown point of about 29%. | 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 ↗ |
|
|