Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Negatīvā binomiālā regresija× | Logistiskā regresija× | |
|---|---|---|
| Nozare≠ | Ekonometrija | Pētniecības statistika |
| Saime≠ | Regression model | Process / pipeline |
| Izcelsmes gads≠ | 2011 | 1958 |
| Autors≠ | Hilbe (textbook treatment); generalized linear model framework | David Roxbee Cox |
| Tips≠ | Generalized linear model for count data | Method |
| Pirmavots≠ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Citi nosaukumi | NB regression, NB2 regression, negatif binom regresyonu | logit model, binomial logistic regression, LR |
| Saistītās≠ | 4 | 3 |
| Kopsavilkums≠ | Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data. | 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. |
| ScholarGateDatu kopa ↗ |
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