Bandingkan metode

	Bootstrap Bayesian (Rubin)	Bootstrap Blok (Blok Bergerak dan Stasioner)	Uji Permutasi (Randomisasi)
Bidang	Statistika	Statistika	Statistika
Keluarga	Regression model	Regression model	Regression model
Tahun asal	1981	1989	2005
Pencetus	Rubin (1981); large-sample theory by Lo (1987)	Künsch (moving block, 1989); Politis & Romano (stationary, 1994)	Good (2005); Edgington & Onghena (2007); resampling tradition
Tipe	Resampling / posterior simulation	Resampling inference for dependent data	Nonparametric resampling test
Sumber perintis	Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134.	Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Alias	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary)	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Terkait	5	5	5
Ringkasan	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).	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.