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Control charts for monitoring the median in non-negative asymmetric data

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Abstract

Control charts are commonly used for monitoring the mean of processes. However, there are practical applications in which asymmetric data are the standard. In these scenarios, the use of robust statistics, such as the median, is advantageous over the mean. Based on this, we propose an empirical control chart for monitoring the median of a wide class of distributions, known as the log-symmetric class. Closed-form estimators, which perform better than the maximum likelihood estimator, are considered. Simulation studies are carried out with the following objectives: to evaluate the in-control and the out-control average run length; to evaluate the behavior of the control limits; and to compare the proposed method with a naive method based on the asymptotic distribution of the three estimators. The results indicate that the proposed approach presents better in-control average run length than the naive method and better power of detection for negative shifts in the median. A practical use of the proposed approach is illustrated with a real engineering problem, followed by a goodness of fit based on AIC and BIC, considering the most common asymmetric distributions. We also perform a residual analysis with the chosen distribution to verify its fit. Finally, based on the chosen distribution the proposed method indicates that there is an out-of-control point in phase II, which is not detected by the naive approach. Therefore, showing a gain in using the proposed method.

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Availability of data and material

The dataset used in the application section is available in the follow web page: https://github.com/lucasdofs/Control-Chart-Log-Symmetric-class

Code availability

The code is available in the follow web page: https://github.com/lucasdofs/Control-Chart-Log-Symmetric-class.

References

  • Balakrishnan N, Saulo H, Bourguignon M, Zhu X (2017) On moment-type estimators for a class of log-symmetric distributions. Comput Stat 32(4):1339–1355

    Article  MathSciNet  Google Scholar 

  • Bilal M, Mohsin M, Aslam M (2021) Weibull-exponential distribution and its application in monitoring industrial process. Mathematical Problems in Engineering 2021

  • Bourguignon M, Ho LL, Fernandes FH (2020) Control charts for monitoring the median parameter of Birnbaum-Saunders distribution. Qual Reliab Eng Int

  • Burkhalter R, Lio Y (2021) Bootstrap control charts for the generalized pareto distribution percentiles. Chilean J Stat 12(1):37–52

    MathSciNet  Google Scholar 

  • Davison AC, Hinkley DV (1997) Bootstrap methods and their application, vol 1. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Dunn PK, Smyth GK (1996) Randomized quantile residuals. J Comput Graph Stat 5(3):236–244

    Google Scholar 

  • Efron B, Tibshirani RJ (1994) An introduction to the bootstrap. CRC Press, Cambridge

    Book  Google Scholar 

  • Gandy A, Kvaløy JT (2013) Guaranteed conditional performance of control charts via bootstrap methods. Scand J Stat 40(4):647–668

    Article  MathSciNet  Google Scholar 

  • Graham MA, Chakraborti S, Mukherjee A (2014) Design and implementation of cusum exceedance control charts for unknown location. Int J Prod Res 52(18):5546–5564

    Article  Google Scholar 

  • Grimshaw SD, Alt FB (1997) Control charts for quantile function values. J Qual Technol 29(1):1–7

    Article  Google Scholar 

  • Hodges JL, Lehmann EL (1963) Estimates of location based on rank tests. Ann Math Stat 34:598–611

    Article  MathSciNet  Google Scholar 

  • Huang WH, Wang H, Yeh AB (2016) Control charts for the lognormal mean. Qual Reliab Eng Int 32(4):1407–1416

    Article  Google Scholar 

  • Janacek G, Meikle S (1997) Control charts based on medians. J R Stat Soc Ser D 46(1):19–31

    Article  Google Scholar 

  • John B, Subhani S (2020) A modified control chart for monitoring non-normal characteristics. Int J Product Qual Manage 29(3):309–328

    Article  Google Scholar 

  • Johnson NL, Kotz S, Balakrishnan N (1995) Continuous univariate distributions, vol 2. Wiley Online Library

  • Kanji G, Arif OH (2000) Median rankit control chart by the quantile approach. J Appl Stat 27(6):757–770

    Article  Google Scholar 

  • Karagöz D (2018) Asymmetric control limits for range chart with simple robust estimator under the non-normal distributed process. Math Sci 12(4):249–262

    Article  MathSciNet  Google Scholar 

  • Khoo M, Atta AMA, Chen C (2009) Proposed X and S control charts for skewed distributions. In: 2009 IEEE international conference on industrial engineering and engineering management, IEEE, pp 389–393

  • Khoo MB (2005) A control chart based on sample median for the detection of a permanent shift in the process mean. Qual Eng 17(2):243–257

    Article  Google Scholar 

  • Kozubowski TJ, Podgórski K (2003) Log-laplace distributions. Int Math J 3:467–495

    MathSciNet  MATH  Google Scholar 

  • Lahcene B (2015) Control charts for skewed distributions: Johnson’s distributions. Int J Stat Med Res 4(2):217–223

    Article  Google Scholar 

  • Lio YL, Park C (2008) A bootstrap control chart for Birnbaum-Saunders percentiles. Qual Reliab Eng Int 24(5):585–600

    Article  Google Scholar 

  • Nadarajah S (2004) Reliability for laplace distributions. Math Prob Eng

  • Nelson LS (1982) Control chart for medians. J Qual Technol 14(4):226–227

    Article  Google Scholar 

  • Page ES (1954) Continuous inspection schemes. Biometrika 41(1/2):100–115

    Article  MathSciNet  Google Scholar 

  • Park HI (2009) Median control charts based on bootstrap method. Commun Stat Simul Comput 38(3):558–570

    Article  MathSciNet  Google Scholar 

  • Puig P (2008) A note on the harmonic law: a two-parameter family of distributions for ratios. Stat Prob Lett 78(3):320–326

    Article  MathSciNet  Google Scholar 

  • R Core Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/

  • Roberts S (1959) Control chart tests based on geometric moving averages. Technometrics 1(3):239–250

    Article  Google Scholar 

  • Sales LOF, Pinho ALS, Medeiros FMC, Bourguignon M (2021) Control chart for monitoring the mean in symmetric data. Chilean J Stat 12(1):37–52

    MathSciNet  Google Scholar 

  • Shen H, Brown LD, Zhi H (2006) Efficient estimation of log-normal means with application to pharmacokinetic data. Stat Med 25(17):3023–3038

    Article  MathSciNet  Google Scholar 

  • Shewhart WA (1931) Economic control of quality of manufactured product. ASQ Quality Press, Milwaukee

    Google Scholar 

  • Shimizu K (1988) Point estimation, chap. 2 in lognormal distributions: Theory and applications, el crow and k. shimizu eds

  • Stasinopoulos DM, Rigby RA et al (2007) Generalized additive models for location scale and shape (gamlss) in r. J Stat Softw 23(7):1–46

    Article  Google Scholar 

  • Van Buren M, Watt W, Marsalek J (1997) Application of the log-normal and normal distributions to stormwater quality parameters. Water Res 31(1):95–104

    Article  Google Scholar 

  • Vanegas LH, Paula GA (2016) Log-symmetric distributions: statistical properties and parameter estimation. Brazil J Probab Stat 30(2):196–220

    MathSciNet  MATH  Google Scholar 

  • Zhang L, Chen G, Castagliola P (2009) On t and EWMA t charts for monitoring changes in the process mean. Qual Reliab Eng Int 25(8):933–945

    Article  Google Scholar 

Download references

Acknowledgements

To the reviewers for valuable contributions to this paper. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Finance Code 001 and CNPq-Brazil (421656/2018-2).

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance Code 001 and CNPq-Brazil (421656/2018-2).

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Correspondence to Lucas O. F. Sales.

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Appendix A

Appendix A

In this appendix, we present the detailed tables of the simulation study performed. The comments and highlights about the results are present in Sect. 4. See Tables 678, 910111213.

Table 6 Control limits, ARL of the proposed and naive methods for PEI and II, based on log-normal distribution, considering \(\alpha =0.0027\) (\(ARL_0= 370.4\))
Table 7 Control limits, ARL of the proposed and naive methods for bias-corrected PEIII, based on log-normal distribution, considering \(\alpha =0.0027\) (\(ARL_0= 370.4\))
Table 8 Control limits, ARL of the proposed and naive methods for PEI and II, based on log-normal distribution, considering \(\alpha =0.00198\) (\(ARL_0= 500\))
Table 9 Control limits, ARL of the proposed and naive methods for bias-corrected PEIII, based on log-normal distribution, considering \(\alpha =0.00198\) (\(ARL_0= 500\))
Table 10 Control limits, ARL of the proposed and naive methods for PEI and II, based on log-Laplace distribution, considering \(\alpha =0.0027\) (\(ARL_0= 370.4\))
Table 11 Control limits, ARL of the proposed and naive methods for corrected-bias PEIII, based on log-Laplace distribution, considering \(\alpha =0.0027\) (\(ARL_0= 370.4\))
Table 12 Control limits, ARL of the proposed and naive methods for PEI and II, based on log-Laplace distribution, considering \(\alpha =0.00198\) (\(ARL_0= 500\))
Table 13 Control limits, ARL of the proposed and naive methods for bias-corrected PEIII, based on log-Laplace distribution, considering \(\alpha =0.00198\) (\(ARL_0= 500\))

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Sales, L.O.F., Pinho, A.L.S., Bourguignon, M. et al. Control charts for monitoring the median in non-negative asymmetric data. Stat Methods Appl 31, 1037–1068 (2022). https://doi.org/10.1007/s10260-022-00624-7

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