Adaptive kernel estimation of spatial relative risk
Adaptive kernel estimation of spatial relative risk
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DOI：
10.1002/sim.3995
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发表时间：
2010-10-15
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.;Hazelton, Martin L.
核平滑通常用于根据疾病病例的点位置和地理区域的采样控制来估计相对风险。通常，采用固定带宽核估计，尽管当基础密度表现出地理流行病学中常见的空间不均匀性类型时，这种方法遇到了广泛认可的问题。更直观的方法是利用空间自适应的可变平滑参数。在本文中，我们通过渐近分析和模拟研究来检查自适应核估计器的属性，发现在这两种情况下都优于固定核方法。我们还研究了自适应相对风险估计器实施的实际问题（包括带宽选择和边界校正），并开发了一种计算成本低廉的方法来生成容差轮廓，以突出显示风险显着升高的区域。版权所有 (C) 2010 约翰·威利父子有限公司
Kernel smoothing is routinely used for the estimation of relative risk based on point locations of disease cases and sampled controls over a geographical region. Typically, fixed-bandwidth kernel estimation has been employed, despite the widely recognized problems experienced with this methodology when the underlying densities exhibit the type of spatial inhomogeneity frequently seen in geographical epidemiology. A more intuitive approach is to utilize a spatially adaptive, variable smoothing parameter. In this paper, we examine the properties of the adaptive kernel estimator by both asymptotic analysis and a simulation study, finding advantages over the fixed kernel approach in both the cases. We also look at practical issues with implementation of the adaptive relative risk estimator (including bandwidth choice and boundary correction), and develop a computationally inexpensive method for generating tolerance contours to highlight areas of significantly elevated risk. Copyright (C) 2010 John Wiley & Sons, Ltd.