IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v293y2021i1p203-218.html
   My bibliography  Save this article

The effect of measurement errors on the performance of the Exponentially Weighted Moving Average control charts for the Ratio of Two Normally Distributed Variables

Author

Listed:
  • Nguyen, H.D.
  • Tran, K.P.
  • Tran, K.D.

Abstract

Investigating the effect of measurement errors on the control chart monitoring the ratio of two normal random variables is an important task to facilitate the use of this kind of control chart in practice. Moreover, a deep insight into the problem can help practitioners to find a way to reduce unexpected impacts of measurement errors on the chart performance. This paper provides a study on the performance of the exponentially weighted moving average control chart monitoring the ratio in the presence of measurement errors. We extend the linear covariate error model applied in previous studies to a more general situation, which makes the study more realistic. The numerical results show that although the precision error and the accuracy error have negative influences on the proposed chart performance when these errors are not large these influences are not significant.

Suggested Citation

  • Nguyen, H.D. & Tran, K.P. & Tran, K.D., 2021. "The effect of measurement errors on the performance of the Exponentially Weighted Moving Average control charts for the Ratio of Two Normally Distributed Variables," European Journal of Operational Research, Elsevier, vol. 293(1), pages 203-218.
    • Handle: RePEc:eee:ejores:v:293:y:2021:i:1:p:203-218
    DOI: 10.1016/j.ejor.2020.11.042
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221720310018
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2020.11.042?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tagaras, George, 2017. "New indices for the evaluation of the statistical properties of Bayesian x¯ control charts for short runsAuthor-Name: Nikolaidis, Yiannis," European Journal of Operational Research, Elsevier, vol. 259(1), pages 280-292.
    2. Song, Zhi & Mukherjee, Amitava & Liu, Yanchun & Zhang, Jiujun, 2019. "Optimizing joint location-scale monitoring – An adaptive distribution-free approach with minimal loss of information," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1019-1036.
    3. K.P. Tran & P. Castagliola & G. Celano, 2016. "Monitoring the ratio of two normal variables using Run Rules type control charts," International Journal of Production Research, Taylor & Francis Journals, vol. 54(6), pages 1670-1688, March.
    4. Bersimis, Sotiris & Koutras, Markos V. & Maravelakis, Petros E., 2014. "A compound control chart for monitoring and controlling high quality processes," European Journal of Operational Research, Elsevier, vol. 233(3), pages 595-603.
    5. Mitra, Amitava & Lee, Kang Bok & Chakraborti, Subhabrata, 2019. "An adaptive exponentially weighted moving average-type control chart to monitor the process mean," European Journal of Operational Research, Elsevier, vol. 279(3), pages 902-911.
    6. Teoh, W.L. & Khoo, Michael B.C. & Castagliola, Philippe & Yeong, W.C. & Teh, S.Y., 2017. "Run-sum control charts for monitoring the coefficient of variation," European Journal of Operational Research, Elsevier, vol. 257(1), pages 144-158.
    7. K.P. Tran & P. Castagliola & G. Celano, 2016. "The performance of the Shewhart-RZ control chart in the presence of measurement error," International Journal of Production Research, Taylor & Francis Journals, vol. 54(24), pages 7504-7522, December.
    8. Song, Zhi & Mukherjee, Amitava & Zhang, Jiujun, 2021. "Some robust approaches based on copula for monitoring bivariate processes and component-wise assessment," European Journal of Operational Research, Elsevier, vol. 289(1), pages 177-196.
    9. Filho, Danilo Marcondes & Valk, Marcio, 2020. "Dynamic VAR model-based control charts for batch process monitoring," European Journal of Operational Research, Elsevier, vol. 285(1), pages 296-305.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Johannssen, Arne & Chukhrova, Nataliya & Castagliola, Philippe, 2022. "The performance of the hypergeometric np chart with estimated parameter," European Journal of Operational Research, Elsevier, vol. 296(3), pages 873-899.
    2. Song, Zhi & Mukherjee, Amitava & Liu, Yanchun & Zhang, Jiujun, 2019. "Optimizing joint location-scale monitoring – An adaptive distribution-free approach with minimal loss of information," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1019-1036.
    3. Song, Zhi & Mukherjee, Amitava & Zhang, Jiujun, 2021. "Some robust approaches based on copula for monitoring bivariate processes and component-wise assessment," European Journal of Operational Research, Elsevier, vol. 289(1), pages 177-196.
    4. Wu, Xiangling & Ding, Shusheng, 2023. "The impact of the Bitcoin price on carbon neutrality: Evidence from futures markets," Finance Research Letters, Elsevier, vol. 56(C).
    5. Teoh, W.L. & Khoo, Michael B.C. & Castagliola, Philippe & Yeong, W.C. & Teh, S.Y., 2017. "Run-sum control charts for monitoring the coefficient of variation," European Journal of Operational Research, Elsevier, vol. 257(1), pages 144-158.
    6. Spiros D. Dafnis & Frosso S. Makri & Markos V. Koutras, 2021. "Generalizations of Runs and Patterns Distributions for Sequences of Binary Trials," Methodology and Computing in Applied Probability, Springer, vol. 23(1), pages 165-185, March.
    7. Lei Yong Lee & Michael Boon Chong Khoo & Sin Yin Teh & Ming Ha Lee, 2015. "A Variable Sampling Interval Synthetic Xbar Chart for the Process Mean," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-18, May.
    8. Moura Neto, F. & Souza, P. & de Magalhães, M.S., 2019. "Determining baseline profile by diffusion maps," European Journal of Operational Research, Elsevier, vol. 279(1), pages 107-123.
    9. Jianwei Gao & Yu Yang & Fangjie Gao & Pengcheng Liang, 2021. "Optimization of Electric Vehicles Based on Frank-Copula- GlueCVaR Combined Wind and Photovoltaic Output Scheduling Research," Energies, MDPI, vol. 14(19), pages 1-15, September.
    10. Yamaguchi, Hikaru & Murakami, Hidetoshi, 2023. "The multi-aspect tests in the presence of ties," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    11. Nasir Abbas & Mu’azu Ramat Abujiya & Muhammad Riaz & Tahir Mahmood, 2020. "Cumulative Sum Chart Modeled under the Presence of Outliers," Mathematics, MDPI, vol. 8(2), pages 1-30, February.
    12. Zhao, Xian & Wang, Xiaoyue & Sun, Ge, 2015. "Start-up demonstration tests with sparse connection," European Journal of Operational Research, Elsevier, vol. 243(3), pages 865-873.
    13. Shusheng Ding & Zhipan Yuan & Fan Chen & Xihan Xiong & Zheng Lu & Tianxiang Cui, 2021. "Impact persistence of stock market risks in commodity markets: Evidence from China," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-22, November.
    14. Chang, Hsing-Ming & Chang, Yung-Ming & Fu, Winnie H.W. & Lee, Wan-Chen, 2018. "On limiting theorems for conditional causation probabilities of multiple-run-rules," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 151-156.
    15. Wenhui Liu & Zhonghua Li & Zhaojun Wang, 2022. "Monitoring of Linear Profiles Using Linear Mixed Model in the Presence of Measurement Errors," Mathematics, MDPI, vol. 10(24), pages 1-17, December.
    16. Pablo Bonilla-Escribano & David Ramírez & Alejandro Porras-Segovia & Antonio Artés-Rodríguez, 2020. "Assessment of Variability in Irregularly Sampled Time Series: Applications to Mental Healthcare," Mathematics, MDPI, vol. 9(1), pages 1-18, December.
    17. Sangahn Kim & Mehmet Turkoz, 2022. "Bayesian sequential update for monitoring and control of high-dimensional processes," Annals of Operations Research, Springer, vol. 317(2), pages 693-715, October.
    18. Zhang, Jinchun & Hou, Jinxiu & Zhang, Zichuan, 2022. "Real-time identification of out-of-control and instability in process parameter for gasification process: Integrated application of control chart and kalman filter," Energy, Elsevier, vol. 238(PB).
    19. Li, Yanting & Wu, Zhenyu, 2020. "A condition monitoring approach of multi-turbine based on VAR model at farm level," Renewable Energy, Elsevier, vol. 166(C), pages 66-80.
    20. Amir Ahmadi-Javid & Mohsen Ebadi, 2017. "Remarks on Bayesian Control Charts," Papers 1712.02860, arXiv.org, revised Dec 2017.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:293:y:2021:i:1:p:203-218. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.