方法对比

	贝叶斯分位数回归	贝叶斯广义线性模型
领域	统计学	统计学
方法族	Regression model	Regression model
起源年份	2001–2011	1989 (GLM); 1995 (Bayesian BDA)
提出者	Kozumi & Kobayashi; building on Yu & Moyeed (2001)	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.
类型	Bayesian semiparametric regression	Bayesian regression model
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.	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
别名	BQR, Bayesian quantile regression model, asymmetric Laplace Bayesian regression, posterior quantile regression	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM
相关	6	6
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.	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.