পদ্ধতির তুলনা করুন

	বেয়েশীয় বুটস্ট্র্যাপ (রুবিন)	BCa বুটস্ট্র্যাপ (বায়াস-সংশোধিত এবং ত্বরান্বিত)	বুুটস্ট্র্যাপ অনুমান	পারমুটেশন (র‍্যান্ডমাইজেশন) টেস্ট
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Regression model	Regression model	Regression model	Regression model
উদ্ভবের বছর	1981	1987	1979	2005
প্রবর্তক	Rubin (1981); large-sample theory by Lo (1987)	Bradley Efron	Bradley Efron	Good (2005); Edgington & Onghena (2007); resampling tradition
ধরন	Resampling / posterior simulation	Resampling confidence interval	Resampling-based inference	Nonparametric resampling test
মৌলিক উৎস	Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134.	Efron, B. (1987). Better Bootstrap Confidence Intervals. Journal of the American Statistical Association, 82(397), 171-185.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
অপর নাম	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	BCa Bootstrap (Bias-Corrected Accelerated), bias-corrected accelerated bootstrap, BCa confidence interval	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
সম্পর্কিত	5	5	5	5
সারসংক্ষেপ	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 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 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.