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Non parametric space-time modeling of SO_2 in presence of many missing data

机译：存在许多缺失数据的SO_2的非参数时空建模

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Given pollution measurement from a network of monitoring sites in the area of a city and over an extended period of time, an important problem is to identify the spatial and temporal structure of the data. In this paper we focus on the identification and estimate of a statistical non parametric model to analyse the SO_2 in the city of Padua, where data are collected by some fixed stations and some mobile stations moving without any specific rule in different new locations. The impact of the use of mobile stations is that for each location there are times when data was not collected. Assuming temporal stationarity and spatial isotropy for the residuals of an additive model for the logarithm of SO_2 concentration, we estimate the semivariogram using a kernel-type estimator. Attempts are made to avoid the assumption of spatial isotropy. Bootstrap confidence bands are obtained for the spatial component of the additive model that is a deterministic function which defines the spatial structure. Finally, an example is proposed to design an optimal network for the mobiles monitoring stations in a fixed future time, given all the information available.
机译：从城市区域内的监视站点网络进行长时间的污染测量，在很长一段时间内，一个重要的问题是确定数据的时空结构。在本文中，我们着重于统计非参数模型的识别和估计，以分析帕多瓦市的SO_2，该城市的数据是由一些固定站和一些移动站在不同的新位置移动而没有任何特定规则的。使用移动站的影响是，对于每个位置，有时都没有收集数据。假设SO_2浓度对数的加性模型的残差的时间平稳性和空间各向同性，我们使用核型估计器估计半变异函数。尝试避免假定空间各向同性。为加性模型的空间分量获得了自举置信带，这是确定空间结构的确定性函数。最后，给出一个示例，在给定所有可用信息的情况下，为固定的未来时间内的移动台监控站设计最佳网络。

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