Parallel discretization of the Markov chain approximation for the autoregressive moving average chart

DOI10.1080/03610918.2018.1463543OpenAlexW2900443738WikidataQ128993758 ScholiaQ128993758MaRDI QIDQ5087543

Chang-Ho Jihn, M. Mujiya Ulkhaq

Publication date: 1 July 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2018.1463543

zbMATH Keywords

computational costMarkov chain approximationaverage run lengthARMA chartstate space discretization

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Simultaneous prediction intervals for multiple forecasts based on Bonferroni and product-type inequalities
• Adaptive CUSUM control chart with variable sampling intervals
• Time Series Analysis: Forecasting and Control, 5th Edition, by George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel and Greta M. Ljung, 2015. Published by John Wiley and Sons Inc., Hoboken, New Jersey, pp. 712. ISBN: 978-1-118-67502-1
• A Markov Chain Model for the Multivariate Exponentially Weighted Moving Averages Control Chart
• AVERAGE RUN LENGTH COMPUTATION OF ARMA CHARTS FOR STATIONARY PROCESSES
• A Class of Markov Chain Models for Average Run Length Computations for Autocorrelated Processes
• The computation of average run length and average time to signal: an overview
• An approach to the probability distribution of cusum run length

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