विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| डबल (इटरेटेड) बूटस्ट्रैप× | रिग्रेशन अनुमान के लिए वाइल्ड बूटस्ट्रैप× | |
|---|---|---|
| क्षेत्र | सांख्यिकी | सांख्यिकी |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष | 1986 | 1986 |
| प्रवर्तक≠ | Hall (1986); Beran (1987) | Wu (1986); refined by Davidson & Flachaire (2008) |
| प्रकार≠ | Resampling calibration (nested bootstrap) | Resampling-based regression inference |
| मौलिक स्रोत≠ | Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452. DOI ↗ | Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295. DOI ↗ |
| उपनाम | iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap) | wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap |
| संबंधित | 5 | 5 |
| सारांश≠ | The double bootstrap is a resampling method that calibrates a bootstrap confidence interval with a second, nested layer of bootstrap to bring its actual coverage closer to the nominal level. Introduced by Hall (1986) and Beran (1987), it is especially valuable for small samples and skewed distributions where a single-layer bootstrap under-covers. | 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. |
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