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A Class of Distribution-Free Exponentially Weighted Moving Average Schemes for Joint Monitoring of Location and Scale Parameters

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Distribution-Free Methods for Statistical Process Monitoring and Control

Abstract

In this chapter, we investigate and compare six distribution-free exponentially weighted moving average (EWMA) schemes for simultaneously monitoring the location and scale parameters of a univariate continuous process. More precisely, we consider a well-known distribution-free EWMA scheme based on the Lepage statistic, and we propose five new EWMA schemes for the same purpose. One of the five new schemes is based on the maximum of EWMA of two individual components, one for the location parameter and the other for the scale parameter, of the Lepage statistic. Such a component-wise combined EWMA is referred to as the cEWMA. Further, we consider an EWMA scheme based on the Cucconi test statistic. We show that it is possible to express the Cucconi statistic as a quadratic combination of two orthogonal statistics, one of which is useful for monitoring the location parameter and the other for monitoring the scale parameter. Such decomposition of the Cucconi statistic is not unique, and one can split it into three different ways. Therefore, we design three more cEWMA schemes corresponding to the decompositions of the Cucconi statistic. We discuss the implementation steps along with an illustration. We perform a detailed comparative study based on Monte Carlo simulation. We observe that the three cEWMA-Cucconi schemes perform very well for various location–scale models.

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Acknowledgements

The first author was supported by the Scientific Research Fund of Liaoning Provincial Education Department of China [grant number LSNQN201912] for carrying out this research. The data were collected by Mr. Divyangshu Singh, a former student of BIT Mesra, India as part of his project during an internship under Second Author. Authors are also grateful to an anonymous reviewer for constructing comments and suggestions.

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Authors and Affiliations

  1. College of Science, Shenyang Agricultural University, Shenyang, 110866, People’s Republic of China

    Zhi Song

  2. Production, Operations and Decision Sciences Area, XLRI-Xavier School of Management, XLRI Jamshedpur, India

    Amitava Mukherjee

  3. Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, Venezia, Italy

    Marco Marozzi

  4. Department of Mathematics, Liaoning University, Shenyang, 110036, People’s Republic of China

    Zhi Song & Jiujun Zhang

Authors
  1. Zhi Song
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  2. Amitava Mukherjee
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  3. Marco Marozzi
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  4. Jiujun Zhang
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Correspondence to Amitava Mukherjee .

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Editors and Affiliations

  1. Department of Statistics and Insurance Science, University of Piraeus, Piraeus, Greece

    Markos V. Koutras

  2. Department of Computer Science and Biomedical Informatics, University of Thessaly, Volos, Greece

    Ioannis S. Triantafyllou

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Song, Z., Mukherjee, A., Marozzi, M., Zhang, J. (2020). A Class of Distribution-Free Exponentially Weighted Moving Average Schemes for Joint Monitoring of Location and Scale Parameters. In: Koutras, M., Triantafyllou, I. (eds) Distribution-Free Methods for Statistical Process Monitoring and Control. Springer, Cham. https://doi.org/10.1007/978-3-030-25081-2_6

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  • DOI: https://doi.org/10.1007/978-3-030-25081-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-25080-5

  • Online ISBN: 978-3-030-25081-2

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