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| Gamma-Regression (GLM)× | Logistische Regression× | Quantile Regression× | |
|---|---|---|---|
| Fachgebiet≠ | Statistik | Forschungsstatistik | Ökonometrie |
| Familie≠ | Regression model | Process / pipeline | Regression model |
| Entstehungsjahr≠ | 1989 | 1958 | 1978 |
| Urheber≠ | McCullagh & Nelder (GLM framework) | David Roxbee Cox | Koenker & Bassett |
| Typ≠ | Generalized linear model | Method | Conditional quantile regression |
| Wegweisende Quelle≠ | McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models (2nd ed.). Chapman and Hall. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Aliasnamen | gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM) | logit model, binomial logistic regression, LR | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Verwandt≠ | 4 | 3 | 5 |
| Zusammenfassung≠ | 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. | 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. | 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. |
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