Parameter estimation for ARTA processes

Bahar Biller*, Barry L. Nelson

*Corresponding author for this work

Research output: Contribution to journalConference article

4 Scopus citations

Abstract

Providing accurate and automated input-modeling support is one of the challenging problems in the application of computer simulation. The models incorporated in current input-modeling software packages often fall short of what is needed because they emphasize independent and identically distributed processes, while dependent time-series processes occur naturally in the simulation of many real-life systems. This paper introduces a statistical methodology for fitting stochastic models to dependent time-series input processes. Specifically, an automated and statistically valid algorithm is presented to fit ARTA (Autoregressive-to-Anything) processes with marginal distributions from the Johnson translation system to stationary univariate time-series data. The use of this algorithm is illustrated via a real-life example.

Original languageEnglish (US)
Pages (from-to)255-262
Number of pages8
JournalWinter Simulation Conference Proceedings
Volume1
StatePublished - Dec 1 2002
EventProceedings of the 2002 Winter Simulation Conference - San Diego, CA, United States
Duration: Dec 8 2002Dec 11 2002

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Safety, Risk, Reliability and Quality
  • Chemical Health and Safety
  • Applied Mathematics

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