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| Regresja beta× | Regresja gamma (GLM)× | |
|---|---|---|
| Dziedzina | Statystyka | Statystyka |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2004 | 1989 |
| Twórca≠ | Ferrari & Cribari-Neto | McCullagh & Nelder (GLM framework) |
| Typ≠ | Generalized linear model (beta distribution) | Generalized linear model |
| Źródło pierwotne≠ | Ferrari, S. L. P. & Cribari-Neto, F. (2004). Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815. DOI ↗ | McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman and Hall. DOI ↗ |
| Inne nazwy | beta regression model, proportion regression, Beta Regresyonu | gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM) |
| Pokrewne | 4 | 4 |
| Podsumowanie≠ | 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. | Gamma regression is a generalized linear model that uses the gamma distribution to model a positive, right-skewed continuous outcome. Developed within the GLM framework of McCullagh and Nelder (1989), it is an alternative to ordinary linear regression for variables such as health-care costs, durations, and income. |
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