পদ্ধতির তুলনা করুন

	জ্যাকনাইফ পুনঃনমুনায়ন (Jackknife Resampling)	সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন	পারমুটেশন (র‍্যান্ডমাইজেশন) টেস্ট
ক্ষেত্র	পরিসংখ্যান	অর্থমিতি	পরিসংখ্যান
পরিবার	Regression model	Regression model	Regression model
উদ্ভবের বছর	1956	2019	2005
প্রবর্তক	Quenouille (1956); reviewed by Miller (1974)	Wooldridge (textbook treatment); classical least squares	Good (2005); Edgington & Onghena (2007); resampling tradition
ধরন	Resampling / bias and variance estimation	Linear regression	Nonparametric resampling test
মৌলিক উৎস	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	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	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.