השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| Bootstrap BCa (Bias-Corrected and Accelerated)× | היסק בוטסטרפ× | Bootstrap כפול (איטרטיבי)× | |
|---|---|---|---|
| תחום | סטטיסטיקה | סטטיסטיקה | סטטיסטיקה |
| משפחה | Regression model | Regression model | Regression model |
| שנת המקור≠ | 1987 | 1979 | 1986 |
| הוגה השיטה≠ | Bradley Efron | Bradley Efron | Hall (1986); Beran (1987) |
| סוג≠ | Resampling confidence interval | Resampling-based inference | Resampling calibration (nested bootstrap) |
| מקור מכונן≠ | Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185. DOI ↗ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ | Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452. DOI ↗ |
| כינויים≠ | BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı | iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap) |
| קשורות | 5 | 5 | 5 |
| תקציר≠ | The BCa bootstrap is a resampling method, introduced by Bradley Efron in 1987, that produces more accurate confidence intervals than the plain percentile bootstrap by applying a bias correction and an acceleration adjustment. It is recommended for skewed distributions and small samples. | 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. | 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. |
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