Vertaile menetelmiä

	Beetaregressio	OLS-regressio (Ordinary Least Squares)	Kvanttiiliregressio
Tieteenala	Tilastotiede	Ekonometria	Ekonometria
Menetelmäperhe	Regression model	Regression model	Regression model
Syntyvuosi	2004	2019	1978
Kehittäjä	Ferrari & Cribari-Neto	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett
Tyyppi	Generalized linear model (beta distribution)	Linear regression	Conditional quantile regression
Alkuperäislähde	Ferrari, S. L. P. & Cribari-Neto, F. (2004). Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815.	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	beta regression model, proportion regression, Beta Regresyonu	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
Liittyvät	4	5	5
Tiivistelmä	Beta regression is a generalized linear model introduced by Ferrari and Cribari-Neto (2004) for outcomes that are rates or proportions confined to the open interval (0,1). It models the mean of a beta-distributed response through a link function, making it the natural choice for fractions, probability scores, and proportion indices.	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.