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| ブートストラップ推論× | 最小二乗法 (OLS) 回帰× | |
|---|---|---|
| 分野≠ | 統計学 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 1979 | 2019 |
| 提唱者≠ | Bradley Efron | Wooldridge (textbook treatment); classical least squares |
| 種類≠ | Resampling-based inference | Linear regression |
| 原典≠ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 別名 | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 関連 | 5 | 5 |
| 概要≠ | 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. | 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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