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.
|Number of pages||21|
|Journal||Communications in Statistics - Simulation and Computation|
|State||Published - 21 Apr 2017|
- Clayton copula
- Joe copula
- Newton–Raphson algorithm
- Shewhart control chart