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| Джакнайф семплиране (Jackknife Resampling)× | Квантилна регресия (непараметрични варианти)× | Метод на най-малките квадрати (МНК)× | |
|---|---|---|---|
| Област≠ | Статистика | Статистика | Иконометрия |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 1956 | 1978 | 2019 |
| Създател≠ | Quenouille (1956); reviewed by Miller (1974) | Koenker & Bassett | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Resampling / bias and variance estimation | Quantile regression (nonparametric variants) | Linear regression |
| Основополагащ източник≠ | Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360. DOI ↗ | Koenker, R. & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Други названия | leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme | quantile regression, median regression, distribution-free quantile regression, Kantil Regresyon (Nonparametric Varyantlar) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Свързани | 5 | 5 | 5 |
| Резюме≠ | 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. | Quantile regression, introduced by Koenker and Bassett in 1978, models a chosen conditional quantile (such as the median or the 25th and 75th percentiles) of a continuous outcome rather than its mean. Its nonparametric variants fit these quantile relationships without assuming a distribution for the errors, making them a robust complement to mean-based regression on skewed data. | 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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