R routines for performing estimation and statistical process control under copula-based time series models. Issue 4 (21st April 2017)

• Record Type: Journal Article
• Authors: Emura, Takeshi; Long, Ting-Hsuan; Sun, Li-Hsien
• Is Part Of: Communications in statistics. Volume 46:Issue 4(2017)
• Page Start: 3067
• Page End: 3087
• Publication Date: 2017-04-21
• Subjects: Clayton copula -- Joe copula -- Newton–Raphson algorithm -- Shewhart control chart; 62M10 -- 62H12 -- 62H20 -- 60J20; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2015.1073303
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: Modeling serial dependence in time series is an important step in statistical process control. We provide a set of automatic routines useful for simulating and analyzing time series under a copula-based serial dependence. First, we introduce routines that generate time series data under a given copula. Second, we provide fully automated routines for obtaining maximum likelihood estimates for given time series data and then drawing a Shewhart-type control chart. Finally, real data are analyzed for illustration. We make the routines available as "Copula.Markov" package in R.