방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 회귀 추론을 위한 와일드 부트스트랩× | 최소제곱법(OLS) 회귀× | |
|---|---|---|
| 분야≠ | 통계학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1986 | 2019 |
| 창시자≠ | Wu (1986); refined by Davidson & Flachaire (2008) | Wooldridge (textbook treatment); classical least squares |
| 유형≠ | Resampling-based regression inference | Linear regression |
| 원전≠ | Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 별칭 | wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 관련 | 5 | 5 |
| 요약≠ | The wild bootstrap is a resampling method for regression models with heteroscedastic errors, introduced by Wu (1986) and refined by Davidson and Flachaire (2008). It builds a bootstrap distribution by rescaling each fitted residual with a random sign, so that standard errors and confidence intervals stay valid when the error variance is not constant or the data are clustered. | 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). |
| ScholarGate데이터셋 ↗ |
|
|