পদ্ধতির তুলনা করুন

	দ্বৈত (পুনরাবৃত্ত) বুটস্ট্র্যাপ	বেয়েশীয় বুটস্ট্র্যাপ (রুবিন)	পারমুটেশন (র‍্যান্ডমাইজেশন) টেস্ট
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Regression model	Regression model	Regression model
উদ্ভবের বছর	1986	1981	2005
প্রবর্তক	Hall (1986); Beran (1987)	Rubin (1981); large-sample theory by Lo (1987)	Good (2005); Edgington & Onghena (2007); resampling tradition
ধরন	Resampling calibration (nested bootstrap)	Resampling / posterior simulation	Nonparametric resampling test
মৌলিক উৎস	Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452.	Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
অপর নাম	iterated bootstrap, nested bootstrap, calibrated bootstrap, Çift Bootstrap (Double / Iterated Bootstrap)	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
সম্পর্কিত	5	5	5
সারসংক্ষেপ	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.	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.