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| Béta regresszió× | Regresszió Ordináris Legkisebb Négyzetes (OLS) módszerrel× | Kvantilis regresszió× | |
|---|---|---|---|
| Tudományterület≠ | Statisztika | Ökonometria | Ökonometria |
| Módszercsalád | Regression model | Regression model | Regression model |
| Keletkezés éve≠ | 2004 | 2019 | 1978 |
| Megalkotó≠ | Ferrari & Cribari-Neto | Wooldridge (textbook treatment); classical least squares | Koenker & Bassett |
| Típus≠ | Generalized linear model (beta distribution) | Linear regression | Conditional quantile regression |
| Alapmű≠ | Ferrari, S. L. P. & Cribari-Neto, F. (2004). Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815. DOI ↗ | 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. DOI ↗ |
| Alternatív nevek≠ | 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 |
| Kapcsolódó≠ | 4 | 5 | 5 |
| Összefoglaló≠ | 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. |
| ScholarGateAdatkészlet ↗ |
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