Testing for unusual aggregation of health risk in semiparametric models.
Testing for unusual aggregation of health risk in semiparametric models.
复制标题
测试半参数模型中健康风险的异常聚合。
DOI：
10.1002/sim.3126
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发表时间：
2008
影响因子：
2
通讯作者：
Lawson,Andrew
中科院分区：
文献类型：
--
作者：
Bottai,Matteo;Geraci,Marco;Lawson,Andrew
我们提出了一种使用空间多级半参数模型对感兴趣的区域进行表面估计的方法，其中空间相关性通过与一组节点相关联的随机系数的样条来建模。多组随机效应与整个感兴趣区域的分区相关联，这允许在较大区域的子区域内灵活地测试异常率。为了检验偏离零值的不寻常率，我们推导了一个基于分数的检验统计量，部分地使用了奇异信息问题的一些结果。该检验是稳健的，因为它不需要指定随机效应的联合分布。在一项广泛的模拟研究中，这一总体通用测试显示了正确的水平，并且对所考虑的所有情景的聚类高度敏感。一旦检测到偏离，可以将第二个更精细的节点网格叠加在现有网格上，并且可以应用所提出的程序来测试两个或更多个子区域内的均匀性。该模型适用于肺癌死亡的南卡罗来纳州在2000年和空气中的汞在植物周围的固体废物焚烧炉在新泽西牛津的数据。版权所有© 2007约翰威利父子有限公司。
We present a method for surface estimation over some area of interest using spatial multilevel semiparametric models, in which the spatial correlation is modeled through splines with random coefficients associated with a set of knots. Multiple sets of random effects are associated with partitions of the entire area of interest that allow flexibility for testing unusual rates within sub‐regions of larger areas. To test departures from the null value of no unusual rates, we derive a score‐based test statistic, partially by using some of the results available for singular information problems. The test is robust in that it does not require specifying the joint distribution of the random effect. In an extensive simulation study this overall general test shows correct levels, and it is highly sensitive to clustering across all the scenarios considered. Once a departure is detected, a second, finer grid of knots can be superimposed on the existing grid, and the proposed procedure can be applied to test the homogeneity within two or more sub‐areas. The proposed model is applied to lung cancer deaths in South Carolina in the year 2000 and to data on airborne mercury in vegetation around a solid waste incinerator in Oxford, New Jersey. Copyright © 2007 John Wiley & Sons, Ltd.