论文标题
QGAM：R中的贝叶斯非参数分位数回归建模
qgam: Bayesian non-parametric quantile regression modelling in R
论文作者
论文摘要
广义加性模型（GAM）是灵活的非线性回归模型，可以使用MGCV R软件包提供的近似贝叶斯方法有效地拟合。尽管MGCV提供的GAM方法基于以下假设：响应分布是通过参数建模的，但在这里我们讨论了不需要任何参数假设的更灵活的方法。特别是，本文介绍了QGAM套件，它是MGCV提供快速校准的贝叶斯方法，用于适合R. QGAM的分位数GAM（QGAM）的方法是基于QGAM的pinball损失（2005年）的平稳版本，而不是基于可靠的量子，因此可以实现可满足的量子，并具有相同的量子，并具有相关的量子，并具有相关性的估计。采用Fasiolo，Wood，Zaffran，Nedellec和Goude（2020b）的专业贝叶斯配件框架。在这里，我们详细介绍了如何在QGAM中实现此框架，并提供了示例，说明了如何在实践中使用软件包。
Generalized additive models (GAMs) are flexible non-linear regression models, which can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R package. While the GAM methods provided by mgcv are based on the assumption that the response distribution is modelled parametrically, here we discuss more flexible methods that do not entail any parametric assumption. In particular, this article introduces the qgam package, which is an extension of mgcv providing fast calibrated Bayesian methods for fitting quantile GAMs (QGAMs) in R. QGAMs are based on a smooth version of the pinball loss of Koenker (2005), rather than on a likelihood function, hence jointly achieving satisfactory accuracy of the quantile point estimates and coverage of the corresponding credible intervals requires adopting the specialized Bayesian fitting framework of Fasiolo, Wood, Zaffran, Nedellec, and Goude (2020b). Here we detail how this framework is implemented in qgam and we provide examples illustrating how the package should be used in practice.
