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Introduction of a general class of entropy-based control charts: The Φ-chart

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We introduce a new class of Shewhart control charts, namely the f-chart. This new class is based on the cumulative paired f-divergence that generalizes both the cumulative (residual) entropy and the differential entropy. The f-chart contains several subclasses; of which one has a special case, the G-chart, which uses Gini’s mean difference as a measure of dispersion. We investigate the performance of three of the subclasses of f-charts in a showcase scenario, comparing its average run length under the Gaussian and several alternative distributions relevant to process control. We find especially the new Leik control chart to outperform classical Shewhart charts, which are based on ranks, standard deviation, or Gini’s mean difference. The results imply that monitoring a production process using f-charts results in faster detection of out-of-control processes, which can be crucial for a variety of application areas.
Wydawca
Rocznik
Strony
55--70
Opis fizyczny
Bibliogr. 21 poz., tab., wykr.
Twórcy
  • Department of Statistics and Econometrics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nuremberg, Germany
autor
  • Institute of Management, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nuremberg, Germany
Bibliografia
  • [1] Burbea, J. and Rao, C. (1982) ‘On the convexity of higher order Jensen differences based on entropy functions (Corresp.)’, IEEE Transactions on Information Theory, vol. 28(6), pp. 961–963, https://doi.org/10.1109/TIT.1982.1056573.
  • [2] Burr, I.W. (1967) ‘The effect of non-normality on constants for X and R charts’, Industrial Quality Control, vol. 23(11), pp. 563–569.
  • [3] Chan, L.K., Hapuarachchi, K.P. and Macpherson, B.D. (1988) ‘Robustness of mean E(X) and R charts’, IEEE Transactions on Reliability, vol. 37(1), pp. 117–123, https://doi.org/10.1109/24.3728.
  • [4] Crowder, S.V. (1987) ‘A Simple Method for Studying Run – Length Distributions of Exponentially Weighted Moving Average Charts’, Technometrics, vol. 29(4), pp. 401–407, https://doi.org/10.1080/00401706.1987.10488267.
  • [5] David, H.A. (1968) ‘Miscellanea: Gini’s mean difference rediscovered’, Biometrika, vol. 55(3), pp. 573–575, https://doi.org/10.1093/biomet/55.3.573.
  • [6] Ebrahimi, N. (1996) ‘How to Measure Uncertainty in the Residual Life Time Distribution’, Sankhyā: The Indian Journal of Statistics, Series A (1961–2002), vol. 58(1), pp. 48–56, [Online], Available: http://www.jstor.org/stable/25051082.
  • [7] Ewan, W.D. (1963) ‘When and How to Use Cu-Sum Charts’, Technometrics, vol. 5(1), pp. 1–22, https://doi.org/10.1080/00401706.1963.10490055.
  • [8] Havrda, J. and Charvát, F. (1967) ‘Quantification method of classification processes. Concept of structural a-entropy’, Kybernetika, vol. 3(1), pp. 30–35.
  • [9] Klein, I., Mangold, B. and Doll, M. (2016) ‘Cumulative Paired φ-Entropy’, Entropy, vol. 18(7), 248, https://doi.org/10.3390/e18070248.
  • [10] Leik, R.K. (1966) ‘A Measure of Ordinal Consensus’, The Pacific Sociological Review, vol. 9(2), pp. 85–90, https://doi.org/10.2307/1388242.
  • [11] Liu, B. (2015) Uncertainty Theory, 4th ed., Berlin–Heidelberg: Springer, https://doi.org/10.1007/978-3-662-44354-5.
  • [12] Luca, A. de and Termini, S. (1972) ‘A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory’, Information and Control, vol. 20(4), pp. 301–312, https://doi.org/10.1016/S0019-9958(72)90199-4.
  • [13] Mangold, B. and Konopik, J. (2017) ‘A general class of entropy based control charts’, Fau Discussion Papers in Economics, No. 04/2017, [Online], Available: http://hdl.handle.net/10419/150013.
  • [14] Page, E.S. (1954) ‘Continuous Inspection Schemes’, Biometrika, vol. 41(1/2), pp. 100–115, https://doi.org/10.2307/2333009.
  • [15] Qiu, P. (2013) Introduction to statistical process control, New York: Chapman and Hall/CRC, https://doi.org/10.1201/b15016.
  • [16] Riaz, M. and Saghir, A. (2007) ‘Monitoring Process Variability Using Gini’s Mean Difference’, Quality Technology & Quantitative Management, vol. 4(4), pp. 439–454, https://doi.org/10.1080/16843703.2007.11673164.
  • [17] Saghir, A. and Lin, Z. (2015) ‘Designing of Gini-chart for Exponential, t, Logistic and Laplace Distributions’, Communications in Statistics – Simulation and Computation, vol. 44(9), pp. 2387–2409, https://doi.org/10.1080/03610918.2013.815770.
  • [18] Shewhart, W.A. (1931) Economic control of quality of manufactured products, American Society for Quality Control.
  • [19] Wang, F., Vemuri, B.C., Rao, M. and Chen, Y. (2003) ‘A new & robust information theoretic measure and its application to image alignment’, Information Processing in Medical Imaging. 18th International Conference, IPMI 2003, Ambleside, UK, July 2003, Proceedings, vol. 2732, pp. 388–400, https://doi.org/10.1007/978-3-540-45087-0_33.
  • [20] Wild, C. J. and Seber, G.A.F. (2000) Chance encounters: A first course in data analysis and inference, Wiley.
  • [21] Zhang, C.W., Xie, M., Liu, J.Y. and Goh, T.N. (2007) ‘A control chart for the Gamma distribution as a model of time between events’, International Journal of Production Research, vol. 45(23), pp. 5649–5666, https://doi.org/10.1080/00207540701325082.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1db6dd14-2f15-44ad-a0d8-433bc929dcc3
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