基于INLA-SPDE的导数空间中介

IF 2.1 2区 数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY
Claudio Rubino , Chiara Di Maria , Antonino Abbruzzo , Gioacchino Bono , Germana Garofalo , Giacomo Milisenda , Giada Adelfio
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引用次数: 0

摘要

在许多应用领域中,评估空间域中发生的中介机制可能会引起人们的兴趣。到目前为止,文献中提出的解决这一问题的方法处理的是面数据,并且经常考虑线性模型。在本文中,我们提出了一种在地质统计数据存在的情况下评估中介的方法,通过将集成嵌套拉普拉斯近似(INLA)与基于导数的中介分析方法相结合,该方法允许人们在非线性模型的情况下估计间接影响。我们通过模拟研究考察了忽略中介和结果模型中空间过程的影响,并重点研究了相关过程的情况。为了展示我们方法的有效性,我们还提供了一个生态应用程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Derivative-based spatial mediation with INLA-SPDE
In many applied fields, it may be of interest to evaluate mediational mechanisms occurring in spatial domains. The approaches proposed so far in the literature to address this issue deal with areal data and often consider linear models. In this paper, we propose an approach to assess mediation in the presence of geostatistical data by combining the integrated nested Laplace approximation (INLA) with a derivative-based approach for mediation analysis, which allows one to estimate indirect effects also in the case of nonlinear models. We investigate the effect of ignoring spatial processes in the mediator and the outcome models through a simulation study, focusing also on the case of correlated processes. To show the usefulness of our approach, we also provided an ecological application.
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来源期刊
Spatial Statistics
Spatial Statistics GEOSCIENCES, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
CiteScore
4.00
自引率
21.70%
发文量
89
审稿时长
55 days
期刊介绍: Spatial Statistics publishes articles on the theory and application of spatial and spatio-temporal statistics. It favours manuscripts that present theory generated by new applications, or in which new theory is applied to an important practical case. A purely theoretical study will only rarely be accepted. Pure case studies without methodological development are not acceptable for publication. Spatial statistics concerns the quantitative analysis of spatial and spatio-temporal data, including their statistical dependencies, accuracy and uncertainties. Methodology for spatial statistics is typically found in probability theory, stochastic modelling and mathematical statistics as well as in information science. Spatial statistics is used in mapping, assessing spatial data quality, sampling design optimisation, modelling of dependence structures, and drawing of valid inference from a limited set of spatio-temporal data.
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