Tutorial on kernel estimation of continuous spatial and spatiotemporal relative risk
Tutorial on kernel estimation of continuous spatial and spatiotemporal relative risk
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DOI：
10.1002/sim.7577
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发表时间：
2018-03-30
影响因子：
2
通讯作者：
Hazelton, Martin L.
中科院分区：
文献类型：
--
作者：
Davies, Tilman M.;Marshall, Jonathan C.;Hazelton, Martin L.
核平滑是一种高度灵活且流行的方法，用于估计连续空间数据的概率密度和强度函数。在这个角色中，它还构成了密度比或相对风险面等泛函估计的一个组成部分。最初开发这种函数的流行病学动机是根据在给定地理区域收集的病例和对照样本来检查疾病风险的波动，这些函数也已成功地应用于对空间密度函数的相对比较感兴趣的各种学科。这种多功能性要求相关方法的不断发展和改进，包括使用空间自适应平滑器；基于渐近理论的显着升高的风险测试；扩展到时空域；以及用于评估的新颖计算方法。在本教程论文中，我们回顾了当前的方法，包括估计、计算和推理方面的最新发展。所有技术均在新软件包 sparr 中实现，该软件包可公开用于 R 语言，我们通过一对流行病学示例来说明其使用。
Kernel smoothing is a highly flexible and popular approach for estimation of probability density and intensity functions of continuous spatial data. In this role, it also forms an integral part of estimation of functionals such as the density-ratio or relative risk surface. Originally developed with the epidemiological motivation of examining fluctuations in disease risk based on samples of cases and controls collected over a given geographical region, such functions have also been successfully used across a diverse range of disciplines where a relative comparison of spatial density functions has been of interest. This versatility has demanded ongoing developments and improvements to the relevant methodology, including use spatially adaptive smoothers; tests of significantly elevated risk based on asymptotic theory; extension to the spatiotemporal domain; and novel computational methods for their evaluation. In this tutorial paper, we review the current methodology, including the most recent developments in estimation, computation, and inference. All techniques are implemented in the new software package sparr, publicly available for the R language, and we illustrate its use with a pair of epidemiological examples.