Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовская квантильная регрессия× | Байесовская обобщенная линейная модель× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2001–2011 | 1989 (GLM); 1995 (Bayesian BDA) |
| Автор метода≠ | Kozumi & Kobayashi; building on Yu & Moyeed (2001) | McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al. |
| Тип≠ | Bayesian semiparametric regression | Bayesian regression model |
| Основополагающий источник≠ | Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation, 81(11), 1565–1578. DOI ↗ | 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 |
| Другие названия | BQR, Bayesian quantile regression model, asymmetric Laplace Bayesian regression, posterior quantile regression | Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM |
| Связанные | 6 | 6 |
| Сводка≠ | Bayesian Quantile Regression estimates the full posterior distribution of regression coefficients at any chosen quantile of the outcome. By combining the asymmetric Laplace likelihood with prior distributions over the coefficients, it delivers uncertainty-quantified estimates of conditional quantiles — such as the median, the 10th, or the 90th percentile — without assuming Gaussian errors. | A Bayesian Generalized Linear Model (Bayesian GLM) extends the classical GLM framework by placing prior distributions on the regression coefficients and updating them with data via Bayes' theorem. This yields a full posterior distribution over parameters rather than single point estimates, enabling richer uncertainty quantification and principled incorporation of prior knowledge for any exponential-family outcome. |
| ScholarGateНабор данных ↗ |
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