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| 贝叶斯分位数回归× | 分位数回归× | |
|---|---|---|
| 领域≠ | 统计学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2001–2011 | 1978 |
| 提出者≠ | Kozumi & Kobayashi; building on Yu & Moyeed (2001) | Koenker & Bassett |
| 类型≠ | Bayesian semiparametric regression | Conditional quantile regression |
| 开创性文献≠ | Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation, 81(11), 1565–1578. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| 别名≠ | BQR, Bayesian quantile regression model, asymmetric Laplace Bayesian regression, posterior quantile regression | conditional quantile regression, regression quantiles, Kantil Regresyon |
| 相关≠ | 6 | 5 |
| 摘要≠ | 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. | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
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