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| Uporišna inferencija× | Regresija običnih najmanjih kvadrata (OLS)× | Test permutacije (randomizacije)× | |
|---|---|---|---|
| Područje≠ | Statistika | Ekonometrija | Statistika |
| Obitelj | Regression model | Regression model | Regression model |
| Godina nastanka≠ | 1979 | 2019 | 2005 |
| Tvorac≠ | Bradley Efron | Wooldridge (textbook treatment); classical least squares | Good (2005); Edgington & Onghena (2007); resampling tradition |
| Vrsta≠ | Resampling-based inference | Linear regression | Nonparametric resampling test |
| Temeljni izvor≠ | 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 | Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792 |
| Drugi nazivi | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | randomization test, exact permutation test, re-randomization test, Permütasyon Testi |
| Srodne | 5 | 5 | 5 |
| Sažetak≠ | 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). | 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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