Vertaile menetelmiä

	Bootstrap-estimaatti	OLS-regressio (Ordinary Least Squares)	Theil-Senin estimaattori
Tieteenala	Tilastotiede	Ekonometria	Tilastotiede
Menetelmäperhe	Regression model	Regression model	Regression model
Syntyvuosi	1979	2019	1968
Kehittäjä	Bradley Efron	Wooldridge (textbook treatment); classical least squares	Henri Theil (1950); P. K. Sen (1968)
Tyyppi	Resampling-based inference	Linear regression	Robust linear regression
Alkuperäislähde	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	Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association, 63(324), 1379-1389.
Rinnakkaisnimet	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	Theil-Sen Tahmincisi, Theil-Sen regression, median slope estimator, Sen's slope estimator
Liittyvät	5	5	6
Tiivistelmä	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).	The Theil-Sen estimator is a robust linear regression method that estimates the slope as the median of the slopes computed over all pairs of data points. Introduced by Henri Theil in 1950 and extended by P. K. Sen in 1968, it tolerates outliers in the response with a breakdown point of about 29%.