Joint quantile regression for spatial data
Joint quantile regression for spatial data
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DOI：
10.1111/rssb.12467
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发表时间：
2021-08-23
影响因子：
5.8
通讯作者：
Tokdar, Surya T.
中科院分区：
文献类型：
--
作者：
Chen, Xu;Tokdar, Surya T.
线性分位数回归是一个强大的工具，用于研究预测因子如何在不同分位数水平上不均匀地影响响应。不幸的是，现有的方法发现它非常难以调整观察单元之间的任何依赖性，主要是因为这样的方法不是基于一个完全生成的数据模型。为了分析空间索引数据，我们通过推广Yang和Tokdar的联合分位数回归模型（Journal of the American Statistical Association，2017，112（519），1107-1120）并通过高斯或t-copula过程对观测单位的基础分位数水平表征空间依赖性来解决这一困难。引入贝叶斯半参数方法来进行模型参数的推断和进行空间分位数平滑。提供了一个有效的模型比较标准，特别是在不同的模型规格的尾部重量和尾部依赖之间进行选择。本文介绍了对颗粒物浓度和野火风险的广泛模拟研究和两个真实的应用，以说明与现有替代方案相比，在推理质量、预测准确性和不确定性量化方面取得的实质性进步。
Linear quantile regression is a powerful tool to investigate how predictors may affect a response heterogeneously across different quantile levels. Unfortunately, existing approaches find it extremely difficult to adjust for any dependency between observation units, largely because such methods are not based upon a fully generative model of the data. For analysing spatially indexed data, we address this difficulty by generalizing the joint quantile regression model of Yang and Tokdar (Journal of the American Statistical Association, 2017, 112(519), 1107-1120) and characterizing spatial dependence via a Gaussian or t-copula process on the underlying quantile levels of the observation units. A Bayesian semiparametric approach is introduced to perform inference of model parameters and carry out spatial quantile smoothing. An effective model comparison criteria is provided, particularly for selecting between different model specifications of tail heaviness and tail dependence. Extensive simulation studies and two real applications to particulate matter concentration and wildfire risk are presented to illustrate substantial gains in inference quality, prediction accuracy and uncertainty quantification over existing alternatives.