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| Jackknife'i korduvvalimimeetod× | Mediaanist absoluutse hälbe (MAD) hindamine× | Tavaline vähimruutude (OLS) regressioon× | |
|---|---|---|---|
| Valdkond≠ | Statistika | Statistika | Ökonomeetria |
| Perekond | Regression model | Regression model | Regression model |
| Tekkeaasta≠ | 1956 | 1974 | 2019 |
| Looja≠ | Quenouille (1956); reviewed by Miller (1974) | Hampel (influence-curve treatment); classical robust statistics | Wooldridge (textbook treatment); classical least squares |
| Tüüp≠ | Resampling / bias and variance estimation | Robust scale estimator | Linear regression |
| Algallikas≠ | Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360. 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 |
| Rööpnimetused | leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme | 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 |
| Seotud | 5 | 5 | 5 |
| Kokkuvõte≠ | The jackknife is a classical resampling method that estimates the bias and variance of a statistic by systematically recomputing it with one observation left out at a time. Introduced by Quenouille in 1956 and later reviewed by Miller in 1974, it predates the bootstrap and remains a simple, deterministic tool for assessing estimator stability. | 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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