{"title":"SpTe2M: An R package for nonparametric modeling and monitoring of spatiotemporal data","authors":"Kai-zuan Yang, Peihua Qiu","doi":"10.1080/00224065.2023.2278795","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Quality Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/00224065.2023.2278795","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
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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