ScholarGate
Asystent

Porównaj metody

Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.

Podwójny (iterowany) bootstrap×Bayesowski Bootstrap (Rubin)×Block Bootstrap (Moving Block i stacjonarny)×
DziedzinaStatystykaStatystykaStatystyka
RodzinaRegression modelRegression modelRegression model
Rok powstania198619811989
TwórcaHall (1986); Beran (1987)Rubin (1981); large-sample theory by Lo (1987)Künsch (moving block, 1989); Politis & Romano (stationary, 1994)
TypResampling calibration (nested bootstrap)Resampling / posterior simulationResampling inference for dependent data
Źródło pierwotneHall, 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 ↗
Inne nazwyiterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap)Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrapmoving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary)
Pokrewne555
PodsumowanieThe 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).
ScholarGateZbiór danych
  1. v1
  2. 2 Źródła
  3. PUBLISHED
  1. v1
  2. 2 Źródła
  3. PUBLISHED
  1. v1
  2. 2 Źródła
  3. PUBLISHED

Przejdź do wyszukiwania Pobierz slajdy

ScholarGatePorównaj metody: Double Bootstrap · Bayesian Bootstrap · Block Bootstrap. Pobrano 2026-06-17 z https://scholargate.app/pl/compare