مقایسهٔ روش‌ها

	بوت استرپ وحشی برای استنتاج رگرسیون	استنتاج بوتسترپ	رگرسیون حداقل مربعات معمولی (OLS)
حوزه	آمار	آمار	اقتصادسنجی
خانواده	Regression model	Regression model	Regression model
سال پیدایش	1986	1979	2019
پدیدآور	Wu (1986); refined by Davidson & Flachaire (2008)	Bradley Efron	Wooldridge (textbook treatment); classical least squares
نوع	Resampling-based regression inference	Resampling-based inference	Linear regression
منبع بنیادین	Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
نام‌های دیگر	wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
مرتبط	5	5	5
خلاصه	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.	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.	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).