Methoden vergleichen

	Quantile Regression	Lasso-Regression
Fachgebiet	Ökonometrie	Maschinelles Lernen
Familie	Regression model	Machine learning
Entstehungsjahr	1978	1996
Urheber	Koenker & Bassett	Tibshirani, R.
Typ	Conditional quantile regression	Regularized linear regression (L1 penalty)
Wegweisende Quelle	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Aliasnamen	conditional quantile regression, regression quantiles, Kantil Regresyon	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Verwandt	5	4
Zusammenfassung	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.