R routines for performing estimation and statistical process control under copula-based time series models

Takeshi Emura, Ting Hsuan Long, Li Hsien Sun

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

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.

Original languageEnglish
Pages (from-to)3067-3087
Number of pages21
JournalCommunications in Statistics - Simulation and Computation
Volume46
Issue number4
DOIs
StatePublished - 21 Apr 2017

Keywords

  • Clayton copula
  • Joe copula
  • Newton–Raphson algorithm
  • Shewhart control chart

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