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| 잭나이프 재표본 추출× | 최소제곱법(OLS) 회귀× | 순열 (무작위화) 검정× | |
|---|---|---|---|
| 분야≠ | 통계학 | 계량경제학 | 통계학 |
| 계열 | Regression model | Regression model | Regression model |
| 기원 연도≠ | 1956 | 2019 | 2005 |
| 창시자≠ | Quenouille (1956); reviewed by Miller (1974) | Wooldridge (textbook treatment); classical least squares | Good (2005); Edgington & Onghena (2007); resampling tradition |
| 유형≠ | Resampling / bias and variance estimation | Linear regression | Nonparametric resampling test |
| 원전≠ | Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792 |
| 별칭 | leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | randomization test, exact permutation test, re-randomization test, Permütasyon Testi |
| 관련 | 5 | 5 | 5 |
| 요약≠ | 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. | 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). | The permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value. |
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