Сравнение методов

	Регрессия Лассо	Логистическая регрессия	Модель с фиксированными эффектами для панельных данных	Квантильная регрессия
Область	Машинное обучение	Статистика исследований	Эконометрика	Эконометрика
Семейство	Machine learning	Process / pipeline	Regression model	Regression model
Год появления	1996	1958	2014	1978
Автор метода	Tibshirani, R.	David Roxbee Cox	Hsiao (textbook treatment); within transformation of panel data	Koenker & Bassett
Тип	Regularized linear regression (L1 penalty)	Method	Panel data regression	Conditional quantile regression
Основополагающий источник	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Другие названия	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	logit model, binomial logistic regression, LR	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli	conditional quantile regression, regression quantiles, Kantil Regresyon
Связанные	4	3	5	5
Сводка	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	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).	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.