Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Gamma-regresjon (GLM)× | Logistisk regresjon× | Poisson- og negativ binomial regresjon× | |
|---|---|---|---|
| Fagfelt≠ | Statistikk | Forskningsstatistikk | Økonometri |
| Familie≠ | Regression model | Process / pipeline | Regression model |
| Opprinnelsesår≠ | 1989 | 1958 | 1998 |
| Opphavsperson≠ | McCullagh & Nelder (GLM framework) | David Roxbee Cox | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| Type≠ | Generalized linear model | Method | Generalized linear model for count data |
| Opprinnelig kilde≠ | 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 ↗ | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| Alias≠ | gamma GLM, gamma generalized linear model, Gamma Regresyonu (GLM) | logit model, binomial logistic regression, LR | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| Relaterte≠ | 4 | 3 | 4 |
| Sammendrag≠ | 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. | 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. |
| ScholarGateDatasett ↗ |
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