Making sequential analysis of environmental monitoring data feasible by simplifying the covariance matrix structure
Making sequential analysis of environmental monitoring data feasible by simplifying the covariance matrix structure
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通过简化协方差矩阵结构使环境监测数据的序贯分析变得可行
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
E. Meelis
中科院分区：
文献类型：
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作者：
M. Schipper;E. Meelis
由于环境监测数据在时间上是连续采集的，因此数据适合于顺序分析。早期的一篇文章提出了一种改进的序贯概率比检验（SPRT）来检验最小相关趋势，假设没有序列相关性，也没有对空间协方差矩阵进行建模。由于模型参数事先未知，因此在分析之前需要最小数量的观测值（nmin）进行估计。在空间协方差矩阵保持非结构化的情况下，如果采样位置的数量增加，则nmin增加。因此，提出了对空间协方差矩阵的假设，从而减少了干扰参数的数量，从而减少了nmin。本文研究。三种简单类型的空间协方差矩阵结构，并为这些类型中的每一种导出经调整的SPRT。此外，我们研究的鲁棒性对偏离假设的空间协方差矩阵结构。仿真研究表明，调整SPRT可以很容易地推导出来，他们在一般情况下对偏离假设类型的空间协方差矩阵是强大的。模拟数据的顺序分析，这是基于监测数据的蝙蝠在荷兰，说明了使用派生SPRT之一。
As environmental monitoring data are collected successively in time, the data are suitable for sequential analysis. An earlier article proposed a refined sequential probability ratio test (SPRT) to test against a minimal relevanttrend, assuming no serial correlations and without modeling the spatial covariance matrix. As the model parameters are unknown in advance, a minimal number of observations(nmin) is required for estimation prior to analysis. Leaving the spatial covariance matrix unstructured, nmin increases if the number of sampling locations increases. Therefore, assumptions on the spatial covariance matrix are proposed, thereby reducing the number of nuisance parameters, thus reducingnmin. This article studies. three simple types of spatial covariance matrix structures and derives an adjusted SPRT for each of these types. Furthermore, we examine the robustness against deviations from the assumed spatial covariance matrix structure. Simulation studies show that adjusted SPRTs can be derived rather easily and that they are in general robust against deviations from the assumed type of spatial covariance matrix. Sequential analysis of simulated data, which are based on monitoring data of bats in the Netherlands, illustrates the use of one of the derived SPRTs.