পদ্ধতির তুলনা করুন

	জ্যাকনাইফ পুনঃনমুনায়ন (Jackknife Resampling)	বুুটস্ট্র্যাপ অনুমান	পারমুটেশন (র‍্যান্ডমাইজেশন) টেস্ট
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Regression model	Regression model	Regression model
উদ্ভবের বছর	1956	1979	2005
প্রবর্তক	Quenouille (1956); reviewed by Miller (1974)	Bradley Efron	Good (2005); Edgington & Onghena (2007); resampling tradition
ধরন	Resampling / bias and variance estimation	Resampling-based inference	Nonparametric resampling test
মৌলিক উৎস	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.	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	leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
সম্পর্কিত	5	5	5
সারসংক্ষেপ	The jackknife is a classical resampling method that estimates the bias and variance of a statistic by systematically recomputing it with one observation left out at a time. Introduced by Quenouille in 1956 and later reviewed by Miller in 1974, it predates the bootstrap and remains a simple, deterministic tool for assessing estimator stability.	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.