Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Bootstrap Ganda (Berulang)× | Bootstrap Bayesian (Rubin)× | Bootstrap Blok (Blok Bergerak dan Stasioner)× | |
|---|---|---|---|
| Bidang | Statistika | Statistika | Statistika |
| Keluarga | Regression model | Regression model | Regression model |
| Tahun asal≠ | 1986 | 1981 | 1989 |
| Pencetus≠ | Hall (1986); Beran (1987) | Rubin (1981); large-sample theory by Lo (1987) | Künsch (moving block, 1989); Politis & Romano (stationary, 1994) |
| Tipe≠ | Resampling calibration (nested bootstrap) | Resampling / posterior simulation | Resampling inference for dependent data |
| Sumber perintis≠ | Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452. DOI ↗ | Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134. DOI ↗ | Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241. DOI ↗ |
| Alias≠ | iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap) | Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap | moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary) |
| Terkait | 5 | 5 | 5 |
| Ringkasan≠ | 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 Bayesian Bootstrap, introduced by Donald B. Rubin in 1981, is a resampling method that produces a Bayesian counterpart to the frequentist bootstrap by assigning each observation a random weight drawn from a Dirichlet distribution. It yields a full posterior distribution for a statistic and allows prior information to be incorporated. | Block bootstrap is a resampling method for dependent, autocorrelated time-series data: instead of resampling single observations, it resamples whole blocks of consecutive observations so the serial-correlation structure is preserved. The moving block variant was introduced by Künsch (1989) and the stationary variant by Politis and Romano (1994). |
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