Regression modelRegression / GLM
贝叶斯分位数回归
贝叶斯分位数回归（Bayesian Quantile Regression）估计了在结果变量的任何选定分位数上回归系数的完整后验分布。通过将非对称拉普拉斯似然与系数的先验分布相结合，它能够提供条件分位数的量化不确定性估计——例如中位数、第10或第90百分位数——而无需假设高斯误差。
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来源
- Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation, 81(11), 1565–1578. DOI: 10.1080/00949655.2010.496117 ↗
- Yu, K., & Zhang, J. (2005). A three-parameter asymmetric Laplace distribution and its extension. Communications in Statistics – Theory and Methods, 34(9–10), 1867–1879. DOI: 10.1080/03610920500199018 ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Quantile Regression. ScholarGate. https://scholargate.app/zh/statistics/bayesian-quantile-regression
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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