AI 中文总结
介绍R包exdqlm用于贝叶斯分位数回归，围绕exDQLMs构建，借助MCMC和LDVB实现后验模拟与快速推断，支持后验不确定性量化，同一接口还支持多种模型及诊断，为时间序列分析提供高效工具。
AI 中文摘要
我们展示了用于贝叶斯分位数回归的R包exdqlm，主要关注时间序列的动态状态空间分位数模型。该包围绕扩展动态分位数线性模型（exDQLMs）构建，其使用扩展非对称拉普拉斯（exAL）族，这是分位数回归中常用非对称拉普拉斯（AL）分布的参数扩展。软件通过马尔可夫链蒙特卡罗（MCMC）提供后验模拟，并通过拉普拉斯 - 德尔塔变分贝叶斯（LDVB）进行快速近似后验推断，支持后验不确定性量化，还为较长时间序列提供计算高效的选项。同一包接口支持带正则化先验的静态exAL分位数回归、给定分位数处非线性输入效应的动态传递函数模型、跨单独拟合分位数的事后后验预测合成、预测以及用于模型评估的定量和可视化诊断。
英文摘要
We present the R package exdqlm for Bayesian quantile regression, with primary emphasis on dynamic state-space quantile models for time series. The package is built around extended dynamic quantile linear models (exDQLMs), which use the extended asymmetric Laplace (exAL) family, a parametric extension of the asymmetric Laplace (AL) distribution commonly used in quantile regression. The software provides posterior simulation via Markov chain Monte Carlo (MCMC) and fast approximate posterior inference via Laplace-delta variational Bayes (LDVB), supporting posterior uncertainty quantification while also providing a computationally efficient option for longer time series. The same package interface supports static exAL quantile regression with regularized priors, dynamic transfer-function models for nonlinear input effects at a given quantile, post hoc posterior-predictive synthesis across separately fitted quantiles, forecasting, and quantitative and visual diagnostics for model evaluation.
Comments47 pages, 9 figures; R package available on CRAN
