विधियों की तुलना करें

	रिग्रेशन अनुमान के लिए वाइल्ड बूटस्ट्रैप	साधारण न्यूनतम वर्ग (OLS) समाश्रयण
क्षेत्र	सांख्यिकी	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1986	2019
प्रवर्तक	Wu (1986); refined by Davidson & Flachaire (2008)	Wooldridge (textbook treatment); classical least squares
प्रकार	Resampling-based regression inference	Linear regression
मौलिक स्रोत	Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
उपनाम	wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
संबंधित	5	5
सारांश	The wild bootstrap is a resampling method for regression models with heteroscedastic errors, introduced by Wu (1986) and refined by Davidson and Flachaire (2008). It builds a bootstrap distribution by rescaling each fitted residual with a random sign, so that standard errors and confidence intervals stay valid when the error variance is not constant or the data are clustered.	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).