Vertaile menetelmiä

	Kvanttiiliregressio	Lasso-regressio	Poisson- ja negatiivinen binomiregressio
Tieteenala	Ekonometria	Koneoppiminen	Ekonometria
Menetelmäperhe	Regression model	Machine learning	Regression model
Syntyvuosi	1978	1996	1998
Kehittäjä	Koenker & Bassett	Tibshirani, R.	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Tyyppi	Conditional quantile regression	Regularized linear regression (L1 penalty)	Generalized linear model for count data
Alkuperäislähde	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Rinnakkaisnimet	conditional quantile regression, regression quantiles, Kantil Regresyon	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Liittyvät	5	4	4
Tiivistelmä	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.	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.	Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred.