方法对比
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| 稳健分位数回归× | 贝叶斯分位数回归× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1993–1997 | 2001–2011 |
| 提出者≠ | Koenker & Bassett (1978); robust extensions by Machado (1993) and He (1997) | Kozumi & Kobayashi; building on Yu & Moyeed (2001) |
| 类型≠ | Robust semiparametric regression | Bayesian semiparametric regression |
| 开创性文献≠ | Koenker, R. (2005). Quantile Regression. Cambridge University Press. ISBN: 978-0521608275 | Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation, 81(11), 1565–1578. DOI ↗ |
| 别名 | robust QR, outlier-resistant quantile regression, bounded-influence quantile regression, RQR | BQR, Bayesian quantile regression model, asymmetric Laplace Bayesian regression, posterior quantile regression |
| 相关 | 6 | 6 |
| 摘要≠ | Robust Quantile Regression estimates conditional quantiles of a response variable while simultaneously downweighting the influence of outliers. By combining the asymmetric loss function of standard quantile regression with bounded-influence or M-estimation weights, it provides reliable quantile estimates even when data contain extreme observations or heavy-tailed error distributions. | 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. |
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