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| Bayes-féle Negatív Binomiális Regresszió× | Negatív binomiális regresszió× | |
|---|---|---|
| Tudományterület≠ | Statisztika | Ökonometria |
| Módszercsalád | Regression model | Regression model |
| Keletkezés éve≠ | 1990s–2000s | 2011 |
| Megalkotó≠ | Gelman, Carlin, Stern, Dunson, Vehtari & Rubin; Cameron & Trivedi | Hilbe (textbook treatment); generalized linear model framework |
| Típus≠ | Bayesian GLM for overdispersed counts | Generalized linear model for count data |
| Alapmű≠ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ |
| Alternatív nevek≠ | Bayesian NB regression, Bayesian negbin model, Bayesian overdispersed count regression, Bayesian NB-2 model | NB regression, NB2 regression, negatif binom regresyonu |
| Kapcsolódó≠ | 6 | 4 |
| Összefoglaló≠ | Bayesian Negative Binomial Regression models non-negative integer count outcomes that exhibit overdispersion — where the variance exceeds the mean — by placing a negative binomial likelihood on the data and specifying prior distributions over the regression coefficients and the dispersion parameter. Posterior inference is typically performed via Markov chain Monte Carlo (MCMC) or variational methods, yielding full posterior distributions rather than point estimates. | 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. |
| ScholarGateAdatkészlet ↗ |
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