Hierarchical additive modeling of nonlinear association with spatial correlations--an application to relate alcohol outlet density and neighborhood assault rates.
Hierarchical additive modeling of nonlinear association with spatial correlations--an application to relate alcohol outlet density and neighborhood assault rates.
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具有空间相关性的非线性关联的分层加性建模——将酒精出口密度和邻里攻击率联系起来的应用。
DOI：
10.1002/sim.3600
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发表时间：
2009
影响因子：
2
通讯作者：
Scribner,RichardAllen
中科院分区：
文献类型：
--
作者：
Yu,Qingzhao;Li,Bin;Scribner,RichardAllen
先前的研究表明，酒精出口和攻击之间存在联系。在本文中，我们探讨了酒精的可用性对攻击的影响，随着时间的推移，在人口普查区的水平。此外，我们使用自然实验来检查突然失去酒精出口是否与攻击暴力的进一步减少有关。这些数据的若干特点提出了统计方面的挑战：（1）协变量之间的关联（例如，每个人口普查区域的酒精出口密度）和攻击率可能是复杂的，因此不能使用没有协变量变换的线性模型来描述，（2）协变量可能彼此高度相关，（3）存在具有缺失输入的多个观测，（4）在人口普查区水平上，袭击率存在空间关联。我们提出了一个分层加性模型，其中的非线性相关性和复杂的相互作用的影响建模使用的多重加性回归树和剩余的空间关联的攻击率，不能在模型中解释的平滑使用条件自回归（CAR）方法。我们开发了一个两阶段算法，将非参数树与CAR连接起来，以寻找与攻击率相关的重要协变量，同时考虑到邻近人口普查区攻击率的空间关联。所提出的方法被应用到洛杉矶的攻击数据（1990-1999年）。为了评估该方法的效率，将结果与从分层线性模型获得的结果进行比较。版权所有© 2009约翰威利父子有限公司。
Previous studies have suggested a link between alcohol outlets and assaults. In this paper, we explore the effects of alcohol availability on assaults at the census tract level over time. In addition, we use a natural experiment to check whether a sudden loss of alcohol outlets is associated with deeper decreasing in assault violence. Several features of the data raise statistical challenges: (1) the association between covariates (for example, the alcohol outlet density of each census tract) and the assault rates may be complex and therefore cannot be described using a linear model without covariates transformation, (2) the covariates may be highly correlated with each other, (3) there are a number of observations that have missing inputs, and (4) there is spatial association in assault rates at the census tract level. We propose a hierarchical additive model, where the nonlinear correlations and the complex interaction effects are modeled using the multiple additive regression trees and the residual spatial association in the assault rates that cannot be explained in the model are smoothed using a conditional autoregressive (CAR) method. We develop a two‐stage algorithm that connects the nonparametric trees with CAR to look for important covariates associated with the assault rates, while taking into account the spatial association of assault rates in adjacent census tracts. The proposed method is applied to the Los Angeles assault data (1990–1999). To assess the efficiency of the method, the results are compared with those obtained from a hierarchical linear model. Copyright © 2009 John Wiley & Sons, Ltd.