Сравнение на методи
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| Оценка на медианното абсолютно отклонение (MAD)× | Метод на най-малките квадрати (МНК)× | Регресия с гребен (Ridge Regression)× | |
|---|---|---|---|
| Област≠ | Статистика | Иконометрия | Машинно обучение |
| Семейство≠ | Regression model | Regression model | Machine learning |
| Година на възникване≠ | 1974 | 2019 | 1970 |
| Създател≠ | Hampel (influence-curve treatment); classical robust statistics | Wooldridge (textbook treatment); classical least squares | Hoerl, A.E. & Kennard, R.W. |
| Тип≠ | Robust scale estimator | Linear regression | L2-regularized linear regression |
| Основополагащ източник≠ | Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Други названия | median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Свързани≠ | 5 | 5 | 4 |
| Резюме≠ | Median Absolute Deviation estimation is a robust measure of statistical dispersion that replaces the standard deviation when outliers are present. Rooted in the influence-curve framework formalised by Hampel (1974), it summarises the spread of a continuous variable using medians instead of means, so a single extreme value cannot distort the result. | 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). | Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated. |
| ScholarGateНабор от данни ↗ |
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