Vertaile menetelmiä

	Robust Regression	Lasso-regressio	OLS-regressio (Ordinary Least Squares)	Kvanttiiliregressio
Tieteenala	Tilastotiede	Koneoppiminen	Ekonometria	Ekonometria
Menetelmäperhe	Regression model	Machine learning	Regression model	Regression model
Syntyvuosi	1964	1996	2019	1978
Kehittäjä	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)	Tibshirani, R.	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett
Tyyppi	Regression with outlier resistance	Regularized linear regression (L1 penalty)	Linear regression	Conditional quantile regression
Alkuperäislähde	Huber, P. J. (1964). Robust estimation of a location parameter. The 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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Rinnakkaisnimet	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
Liittyvät	6	4	5	5
Tiivistelmä	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.	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.	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).	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.