Methoden vergleichen

	Bootstrap-Inferenz	Methode der kleinsten Quadrate (OLS)	Theil-Sen-Schätzer
Fachgebiet	Statistik	Ökonometrie	Statistik
Familie	Regression model	Regression model	Regression model
Entstehungsjahr	1979	2019	1968
Urheber	Bradley Efron	Wooldridge (textbook treatment); classical least squares	Henri Theil (1950); P. K. Sen (1968)
Typ	Resampling-based inference	Linear regression	Robust linear regression
Wegweisende Quelle	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.
Aliasnamen	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
Verwandt	5	5	6
Zusammenfassung	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%.