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Comparative Analysis of Normality Tests: Integrating Classical and Contemporary Approaches

  • Conference paper
  • First Online:
20th International Probabilistic Workshop (IPW 2024)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 494))

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  • 291 Accesses

Abstract

This study conducts a systematic evaluation of several normality tests, essential in statistical analyses and validating assumptions in diverse fields, including finance and energy. The primary contribution lies in extending the Jelito-Pitera test to detect deviations in light-tailed distributions, filling a gap in the range of existing normality tests. Additionally, this research conducts a detailed comparison of recent normality tests, specifically focusing on the Jelito-Pitera test and the Kullback-Leibler information-based test. We assess their effectiveness in identifying deviations not only in heavy-tailed but also in light-tailed distributions. Employing Monte Carlo simulations across various sample sizes and significance levels, our work examines the efficacy of well-established tests like Anderson-Darling, Zhang, Shapiro-Wilk, Shapiro-Francia, and Jarque-Bera, alongside these newer methods. The focus is on their per-formance in different non-normal distributions, aiming to understand their relative strengths and limitations in practical applications. The findings of this research provide a nuanced view of statistical testing methods, emphasizing the adaptation of the Jelito-Pitera test for light tails and offering insights into the comparative effectiveness of both classical and contemporary normality tests under diverse conditions. This study aims to assist researchers and practitioners in selecting the most suitable normality test for their data, thereby improving the precision of data analysis across various research areas.

Supported by organization Centre of Mathematics of the University of Minho, within projects UIDB/00013/2020 https://doi.org/10.54499/UIDB/00013/2020 and UIDP/00013/2020 https://doi.org/10.54499/UIDP/00013/2020.

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References

  1. D’Agostino, R.B.: Goodness-of-Fit-Techniques, vol. 68. CRC Press, London (1986)

    Google Scholar 

  2. Thode, H.C.: Testing for Normality, vol. 164. CRC Press, Boca Raton (2002)

    Book  Google Scholar 

  3. Vasicek, O.: A test for normality based on sample entropy. J. Roy. Stat. Soc. Ser. B (Methodol.) 38(1), 54–59 (1976)

    Article  MathSciNet  Google Scholar 

  4. Jarque, C.M., Bera, A.K.: A test for normality of observations and regression residuals. Int. Stat. Rev. Revue Internationale de Statistique 55(2), 163–172 (1987)

    MathSciNet  Google Scholar 

  5. Stephens, M.A.: EDF statistics for goodness of fit and some comparisons. J. Am. Stat. Assoc. 69(347), 730–737 (1974)

    Article  Google Scholar 

  6. Yazici, B., Yolacan, S.: A comparison of various tests of normality. J. Stat. Comput. Simul. 77(2), 175–183 (2007)

    Article  MathSciNet  Google Scholar 

  7. Meintanis, S.G.: Goodness-of-fit testing by transforming to normality: comparison between classical and characteristic function-based methods. J. Stat. Comput. Simul. 79(2), 205–212 (2009)

    Article  MathSciNet  Google Scholar 

  8. Jelito, D., Pitera, M.: New fat-tail normality test based on conditional second moments with applications to finance. Stat. Pap. 62(5), 2083–2108 (2021)

    Article  MathSciNet  Google Scholar 

  9. Engel, R.F., Granger, C.W.J., Rice, J., Weiss, A.: Semiparametric estimates of the relation between weather and electricity sales. J. Am. Stat. Assoc. 81, 310–320 (1986)

    Article  Google Scholar 

  10. Taylor, J.W., Buizza, R.: Neural network load forecasting with weather ensemble predictions. IEEE Trans. Power Syst. 17, 626–632 (2002)

    Article  Google Scholar 

  11. Jaworski, P., Pitera, M.: The 20-60-20 rule. Discrete Continuous Dyn. Syst. Ser. B. 21(4) (2016)

    Google Scholar 

  12. Alizadeh Noughabi, H.: A new estimator of Kullback-Leibler information and its application in goodness of fit tests. J. Stat. Comput. Simul. 89(10), 1914–1934 (2019)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Centre of Mathematics, Universidade do Minho, 410-057, Braga, Portugal

    Cecilia Castro & Lucas Henriques

  2. Federal Institute of Alagoas, Maceió, AL, 57020-600, Brazil

    Lucas Henriques & Felipe Prata

Authors
  1. Cecilia Castro
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  2. Lucas Henriques
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  3. Felipe Prata
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Corresponding author

Correspondence to Felipe Prata .

Editor information

Editors and Affiliations

  1. ISISE, Department of Civil Engineering, University of Minho, Guimarães, Portugal

    José C. Matos

  2. ISISE, Department of Civil Engineering, University of Minho, Guimarães, Portugal

    Paulo B. Lourenço

  3. ISISE, Department of Civil Engineering, University of Minho, Guimarães, Portugal

    Daniel V. Oliveira

  4. ISISE, Department of Civil Engineering, University of Minho, Guimarães, Portugal

    Jorge Branco

  5. Department of Architecture, Wood and Civil Engineering, Bern University of Applied Sciences, Burgdorf, Switzerland

    Dirk Proske

  6. ISISE, Department of Civil Engineering, University of Minho, Guimarães, Portugal

    Rui A. Silva

  7. ISISE, Department of Civil Engineering, University of Minho, Guimarães, Portugal

    Hélder S. Sousa

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Castro, C., Henriques, L., Prata, F. (2024). Comparative Analysis of Normality Tests: Integrating Classical and Contemporary Approaches. In: Matos, J.C., et al. 20th International Probabilistic Workshop. IPW 2024. Lecture Notes in Civil Engineering, vol 494. Springer, Cham. https://doi.org/10.1007/978-3-031-60271-9_33

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  • DOI: https://doi.org/10.1007/978-3-031-60271-9_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-60270-2

  • Online ISBN: 978-3-031-60271-9

  • eBook Packages: EngineeringEngineering (R0)

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