基于卫星的PM2.5评估的高斯马尔可夫随机场与线性混合模型：来自东北部USA的证据
Gaussian Markov Random Fields versus Linear Mixed Models for satellite-based PM2.5 assessment: Evidence from the Northeastern USA
摘要：
研究空气污染对健康的影响是环境流行病学的一个关键领域。准确估计空气污染的影响需要时空分辨的空气污染数据集，特别是细颗粒物（PM）。卫星技术大大提高了在无法直接测量的地点提供PM评估的能力。间接PM测量是一个统计预测问题。时空统计文献提供了各种预测模型：高斯随机场（GRF）和线性混合模型（LMM），特别是。GRF强调数据中的时空结构，但计算要求很高。LIFE在计算上更容易适应，但需要一些篡改来处理空间和时间。时空统计文献中的最新进展提出通过高斯马尔可夫随机场（GMRF）近似GRF来减轻计算负担。由于LMF和GMRF都是计算上可行的，问题就出现了：哪一个在统计上更好？我们发现，尽管LPERS在环境监测和污染评估中非常受欢迎，但在美国东北部，LPERS在统计学上不如GMRF测量PM。
英文摘要：
Studying the effects of air-pollution on health is a key area in environmental epidemiology. An accurate estimation of air-pollution effects requires spatio-temporally resolved datasets of air-pollution, especially, Fine Particulate Matter (PM). Satellite-based technology has greatly enhanced the ability to provide PM assessments in locations where direct measurement is impossible. Indirect PM measurement is a statistical prediction problem. The spatio-temporal statistical literature offer various predictive models: Gaussian Random Fields (GRF) and Linear Mixed Models (LMM), in particular. GRF emphasize the spatio-temporal structure in the data, but are computationally demanding to fit. LMMs are computationally easier to fit, but require some tampering to deal with space and time. Recent advances in the spatio-temporal statistical literature propose to alleviate the computation burden of GRFs by approximating them with Gaussian Markov Random Fields (GMRFs). Since LMMs and GMRFs are both computationally feasible, the question arises: which is statistically better? We show that despite the great popularity of LMMs in environmental monitoring and pollution assessment, LMMs are statistically inferior to GMRF for measuring PM in the Northeastern USA.
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