Vertaile menetelmiä

	Bayesilainen Bootstrap (Rubin)	Bootstrap-estimaatti	OLS-regressio (Ordinary Least Squares)
Tieteenala	Tilastotiede	Tilastotiede	Ekonometria
Menetelmäperhe	Regression model	Regression model	Regression model
Syntyvuosi	1981	1979	2019
Kehittäjä	Rubin (1981); large-sample theory by Lo (1987)	Bradley Efron	Wooldridge (textbook treatment); classical least squares
Tyyppi	Resampling / posterior simulation	Resampling-based inference	Linear regression
Alkuperäislähde	Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134.	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
Rinnakkaisnimet	Bayesian Bootstrap (Rubin), Rubin bootstrap, Dirichlet-weighted bootstrap	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Liittyvät	5	5	5
Tiivistelmä	The Bayesian Bootstrap, introduced by Donald B. Rubin in 1981, is a resampling method that produces a Bayesian counterpart to the frequentist bootstrap by assigning each observation a random weight drawn from a Dirichlet distribution. It yields a full posterior distribution for a statistic and allows prior information to be incorporated.	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).