विधियों की तुलना करें

	बूटस्ट्रैप अनुमान	माध्यिका निरपेक्ष विचलन (MAD) आकलन	साधारण न्यूनतम वर्ग (OLS) समाश्रयण	क्रमचय (यादृच्छिकीकरण) परीक्षण
क्षेत्र	सांख्यिकी	सांख्यिकी	अर्थमिति	सांख्यिकी
परिवार	Regression model	Regression model	Regression model	Regression model
उद्भव वर्ष	1979	1974	2019	2005
प्रवर्तक	Bradley Efron	Hampel (influence-curve treatment); classical robust statistics	Wooldridge (textbook treatment); classical least squares	Good (2005); Edgington & Onghena (2007); resampling tradition
प्रकार	Resampling-based inference	Robust scale estimator	Linear regression	Nonparametric resampling test
मौलिक स्रोत	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393.	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
उपनाम	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
संबंधित	5	5	5	5
सारांश	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.	Median Absolute Deviation estimation is a robust measure of statistical dispersion that replaces the standard deviation when outliers are present. Rooted in the influence-curve framework formalised by Hampel (1974), it summarises the spread of a continuous variable using medians instead of means, so a single extreme value cannot distort the result.	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.