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引用次数: 0
摘要
时空数据在实践中很常见。这类数据通常具有复杂的结构,很难用参数统计模型来描述。因此,要有效地分析时空数据往往具有挑战性,因为现有文献中的大多数统计方法和软件包都是基于参数建模的,无法正确处理某些应用。本文介绍了新的 R 软件包 SpTe2M,它是为实现一些最新的非参数方法而开发的,用于时空数据的建模和监测。该软件包为时空数据的非参数建模和随时间顺序的动态空间过程监测提供了分析工具。它可用于不同的应用,包括疾病监测、环境监测等。我们使用 2012-2014 年间观察到的佛罗里达州流感样疾病数据和 2014-2016 年间收集到的中国 PM2.5 浓度数据演示了该软件包的使用。
SpTe2M: An R package for nonparametric modeling and monitoring of spatiotemporal data
Spatio-temporal data are common in practice. Such data often have complicated structures that are difficult to describe by parametric statistical models. Thus, it is often challenging to analyze spatio-temporal data effectively since most existing statistical methods and software packages in the literature are based on parametric modeling and cannot handle certain applications properly. This paper introduces the new R package SpTe2M , which is developed for implementing some recent nonparametric methods for modeling and monitoring spatio-temporal data. This package provides analytic tools for modeling spatio-temporal data nonparametrically and for monitoring dynamic spatial processes sequentially over time. It can be used for different applications, including disease surveillance, environmental monitoring, and more. The use of the package is demonstrated using the Florida influenza-like illness data observed during 2012-2014 and the PM2.5 concentration data in China collected during 2014-2016.
期刊介绍:
The objective of Journal of Quality Technology is to contribute to the technical advancement of the field of quality technology by publishing papers that emphasize the practical applicability of new techniques, instructive examples of the operation of existing techniques and results of historical researches. Expository, review, and tutorial papers are also acceptable if they are written in a style suitable for practicing engineers.
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