Comparar métodos

	Regresión lineal múltiple robusta	Regresión Lasso
Campo	Estadística	Aprendizaje automático
Familia	Regression model	Machine learning
Año de origen	1964–1980s	1996
Autor original	Peter J. Huber (M-estimators, 1964); extended by Rousseeuw, Yohai, and Maronna	Tibshirani, R.
Tipo	Robust linear regression	Regularized linear regression (L1 penalty)
Fuente seminal	Huber, P. J. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35(1), 73–101.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Alias	robust MLR, M-estimator regression, resistant multiple regression, robust OLS	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Relacionados	6	4
Resumen	Robust multiple linear regression estimates the linear relationship between a continuous outcome and several predictors while being resistant to outliers and violations of the normality assumption. Instead of minimising the sum of squared residuals, it uses a bounded loss function — most commonly Huber's or Tukey's bisquare — so that extreme observations receive limited influence on the estimated coefficients.	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.