Bivariate copulas on the exponentially weighted moving average control chart

Sasigarn Kuvattana; Piyapatr Busababodhin; Yupaporn Areepong; Saowanit Sukparungsee

doi:10.14456/sjst-psu.2016.72

Songklanakarin Journal of Science and Technology (SJST) (Oct 2016)

Bivariate copulas on the exponentially weighted moving average control chart

Sasigarn Kuvattana,
Piyapatr Busababodhin,
Yupaporn Areepong,
Saowanit Sukparungsee

Affiliations

Sasigarn Kuvattana: Department of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bang Sue, Bangkok, 10800 Thailand.
Piyapatr Busababodhin: Department of Mathematics, Faculty of Science, Mahasarakham University, Kantharawichai, Maha Sarakham, 41150 Thailand.
Yupaporn Areepong: Department of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bang Sue, Bangkok, 10800 Thailand.
Saowanit Sukparungsee: Department of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bang Sue, Bangkok, 10800 Thailand.

DOI: https://doi.org/10.14456/sjst-psu.2016.72
Journal volume & issue: Vol. 38, no. 5
pp. 569 – 574

Abstract

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This paper proposes four types of copulas on the Exponentially Weighted Moving Average (EWMA) control chart when observations are from an exponential distribution using a Monte Carlo simulation approach. The performance of the control chart is based on the Average Run Length (ARL) which is compared for each copula. Copula functions for specifying dependence between random variables are used and measured by Kendall’s tau. The results show that the Normal copula can be used for almost all shifts.

Published in Songklanakarin Journal of Science and Technology (SJST)

ISSN: 0125-3395 (Print)
Publisher: Prince of Songkla University
Country of publisher: Thailand
LCC subjects: Technology: Technology (General); Science: Science (General)
Website: https://sjst.psu.ac.th/

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