قارن الطرق

	انحدار لاسو	نموذج التأثيرات الثابتة لبيانات السلاسل الزمنية المقطعية
المجال	تعلم الآلة	الاقتصاد القياسي
العائلة	Machine learning	Regression model
سنة النشأة	1996	2014
صاحب الطريقة	Tibshirani, R.	Hsiao (textbook treatment); within transformation of panel data
النوع	Regularized linear regression (L1 penalty)	Panel data regression
المصدر التأسيسي	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
الأسماء البديلة	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
ذات صلة	4	5
الملخص	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.	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).