مقایسهٔ روش‌ها

	رگرسیون لسو	رگرسیون حداقل مربعات هرس‌شده (LTS)	رگرسیون حداقل مربعات معمولی (OLS)
حوزه	یادگیری ماشین	آمار	اقتصادسنجی
خانواده	Machine learning	Regression model	Regression model
سال پیدایش	1996	1984	2019
پدیدآور	Tibshirani, R.	Peter J. Rousseeuw	Wooldridge (textbook treatment); classical least squares
نوع	Regularized linear regression (L1 penalty)	Robust linear regression	Linear regression
منبع بنیادین	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
نام‌های دیگر	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	LTS, least trimmed squares regression, trimmed least squares, robust regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
مرتبط	4	5	5
خلاصه	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.	Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers.	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).