Geographically weighted elastic net logistic regression
Geographically weighted elastic net logistic regression
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DOI：
10.1007/s10109-018-0280-7
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发表时间：
2018-09
影响因子：
2.9
通讯作者：
A. Comber;P. Harris
中科院分区：
文献类型：
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作者：
A. Comber;P. Harris
本文开发了一种本地化的方法，弹性网络逻辑回归，扩展以前的研究描述本地化的弹性网络作为一个扩展到本地化的岭回归或本地化的套索。所有这些模型的目标都是捕获跨空间变化的数据关系。地理加权弹性净逻辑回归首先通过模拟实验进行评估，并显示为局部模型选择和减轻局部共线性提供了一种稳健的方法，然后应用于两个案例研究：2016年美国总统选举中的县级投票模式，研究与投票给特朗普相关的社会经济因素的空间结构，和一个物种的存在，不存在的数据集与解释性的环境和气候因素在网格位置覆盖美国大陆。 该方法与其他Logistic回归进行了比较。它提高了预测选举的情况下，只有表现出更大的空间异质性的二进制响应比物种的情况下，研究。模型比较表明，标准的地理加权逻辑回归高估了关系的非平稳性，因为它不能充分处理共线性和模型选择。结果进行了讨论的背景下，预测变量共线性和选择和观察到的异质性。正在进行的工作是调查当地派生的弹性网络参数。
This paper develops a localized approach to elastic net logistic regression, extending previous research describing a localized elastic net as an extension to a localized ridge regression or a localized lasso. All such models have the objective to capture data relationships that vary across space. Geographically weighted elastic net logistic regression is first evaluated through a simulation experiment and shown to provide a robust approach for local model selection and alleviating local collinearity, before application to two case studies: county-level voting patterns in the 2016 USA presidential election, examining the spatial structure of socio-economic factors associated with voting for Trump, and a species presence–absence data set linked to explanatory environmental and climatic factors at gridded locations covering mainland USA. The approach is compared with other logistic regressions. It improves prediction for the election case study only which exhibits much greater spatial heterogeneity in the binary response than the species case study. Model comparisons show that standard geographically weighted logistic regression over-estimated relationship non-stationarity because it fails to adequately deal with collinearity and model selection. Results are discussed in the context of predictor variable collinearity and selection and the heterogeneities that were observed. Ongoing work is investigating locally derived elastic net parameters.