Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовская отрицательная биномиальная регрессия× | Регрессия отрицательного биномиального распределения× | |
|---|---|---|
| Область≠ | Статистика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1990s–2000s | 2011 |
| Автор метода≠ | Gelman, Carlin, Stern, Dunson, Vehtari & Rubin; Cameron & Trivedi | Hilbe (textbook treatment); generalized linear model framework |
| Тип≠ | Bayesian GLM for overdispersed counts | Generalized linear model for count data |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия≠ | Bayesian NB regression, Bayesian negbin model, Bayesian overdispersed count regression, Bayesian NB-2 model | NB regression, NB2 regression, negatif binom regresyonu |
| Связанные≠ | 6 | 4 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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