R routines for performing estimation and statistical process control under copula-based time series models. 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.
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References in zbMATH (referenced in 3 articles , 1 standard article )
Showing results 1 to 3 of 3.
- Huang, Xin-Wei; Wang, Weijing; Emura, Takeshi: A copula-based Markov chain model for serially dependent event times with a dependent terminal event (2021)
- Zhang, Shulin; Zhou, Qian M.; Lin, Huazhen: Goodness-of-fit test of copula functions for semi-parametric univariate time series models (2021)
- Emura, Takeshi; Long, Ting-Hsuan; Sun, Li-Hsien: R routines for performing estimation and statistical process control under copula-based time series models (2017)