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| Бутстрап извод× | Оценка на медианното абсолютно отклонение (MAD)× | Метод на най-малките квадрати (МНК)× | |
|---|---|---|---|
| Област≠ | Статистика | Статистика | Иконометрия |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 1979 | 1974 | 2019 |
| Създател≠ | Bradley Efron | Hampel (influence-curve treatment); classical robust statistics | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Resampling-based inference | Robust scale estimator | Linear regression |
| Основополагащ източник≠ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ | 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 |
| Други названия | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı | 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 |
| Свързани | 5 | 5 | 5 |
| Резюме≠ | Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples. | 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). |
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