DOI QR코드

DOI QR Code

A modified test for multivariate normality using second-power skewness and kurtosis

  • Namhyun Kim (Department of Science, Hongik University)
  • 투고 : 2023.02.07
  • 심사 : 2023.06.09
  • 발행 : 2023.07.31

초록

The Jarque and Bera (1980) statistic is one of the well known statistics to test univariate normality. It is based on the sample skewness and kurtosis which are the sample standardized third and fourth moments. Desgagné and de Micheaux (2018) proposed an alternative form of the Jarque-Bera statistic based on the sample second power skewness and kurtosis. In this paper, we generalize the statistic to a multivariate version by considering some data driven directions. They are directions given by the normalized standardized scaled residuals. The statistic is a modified multivariate version of Kim (2021), where the statistic is generalized using an empirical standardization of the scaled residuals of data. A simulation study reveals that the proposed statistic shows better power when the dimension of data is big.

키워드

과제정보

This work was supported by 2022 Hongik University Research Fund.

참고문헌

  1. Bowman KO and Shenton LR (1975). Omnibus test contours for departures from normality based on ${\sqrt{b_1}}$ and b2, Biometrika, 62, 243-250.
  2. D'Agostino RB and Pearson ES (1973). Tests for departure from normality: Empirical results for the distributions of b2 and ${\sqrt{b_1}}$, Biometrika, 60, 613-622.
  3. D'Agostino RB and Pearson ES (1974). Correction and amendment: Tests for departure from normality: Empirical results for the distributions of b2 and ${\sqrt{b_1}}$, Biometrika, 61, 647-647.
  4. D'Agostino RB and Stephens MA (1986). Goodness-of-Fit Techniques, Marcel Dekker, New York.
  5. Desgagne A and de Micheaux PL (2018). A powerful and interpretable alternative to the Jarque-Bera test of normality based on 2nd-power skewness and kurtosis, using the Rao's score test on the APD family, Journal of Applied Statistics, 45, 2307-2327. https://doi.org/10.1080/02664763.2017.1415311
  6. Doornik JA and Hansen H (2008). An omnibus test for univariate and multivariate normality, Oxford Bulletin of Economics and Statistics, 70, 927-939. https://doi.org/10.1111/j.1468-0084.2008.00537.x
  7. Ebner B and Henze N (2020). Tests for multivariate normality-a critcal review with emphasis on weighted L2-statistics, Test, 29, 845-892. https://doi.org/10.1007/s11749-020-00740-0
  8. Farrell PJ, Salibian-Barrera M, and Naczk K (2007). On tests for multivariate normality and associated simulation studies, Journal of Statistical Computation and Simulation, 77, 1065-1080. https://doi.org/10.1080/10629360600878449
  9. Fang, K-T and Wang Y (1993). Number-Theoretic Methods in Statistics, Chapman & Hall, London.
  10. Fattorini L (1986). Remarks on the use of the Shapiro-Wilk statistic for testing multivariate normality, Statistica, 46, 209-217.
  11. Fisher RA (1936). The use of multiple measurements in taxonomic problems, Annals of Eugenics, 7, 179-188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x
  12. Hanusz Z, Enomoto R, Seo T, and Koizumi K (2018). A Monte Carlo comparison of Jarque-Bera type tests and Henze-Zirkler test of multivariate normality, Communications in Statistics - Simulation and Computation, 47, 1439-1452. https://doi.org/10.1080/03610918.2017.1315771
  13. Henze N (2002) Invariant tests for multivariate normality: A critical review, Statistical Papers, 43, 467-506. https://doi.org/10.1007/s00362-002-0119-6
  14. Henze N and Zirkler B (1990). A class of invariant consistent tests for multivariate normality, Communications in Statistics-Theory and Methods, 19, 3539-3617. https://doi.org/10.1080/03610929008830396
  15. Horswell RL and Looney SW (1992). A comparison of tests for multivariate normality that are based on measures of multivariate skewness and kurtosis, Journal of Statistical Computation and Simulation, 42, 21-38. https://doi.org/10.1080/00949659208811407
  16. Jarque C and Bera A (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals, Economics Letters, 6, 255-259. https://doi.org/10.1016/0165-1765(80)90024-5
  17. Kim N (2004). Remarks on the use of multivariate skewness and kurtosis for testing multivariate normality, The Korean Journal of Applied Statistics, 17, 507-518. https://doi.org/10.5351/KJAS.2004.17.3.507
  18. Kim N (2006). Testing multivariate normality based on EDF statistics, The Korean Journal of Applied Statistics, 19, 39-54. https://doi.org/10.5351/KJAS.2006.19.2.241
  19. Kim N (2015). Tests based on skewness and kurtosis for multivariate normality, Communications for Statistical Applications and Methods, 22, 361-375. https://doi.org/10.5351/CSAM.2015.22.4.361
  20. Kim N (2016). A robustified Jarque-Bera test for multivariate normality, Economics Letters, 140, 48-52. https://doi.org/10.1016/j.econlet.2016.01.007
  21. Kim N (2020). Omnibus tests for multivariate normality based on Mardia's skewness and kurtosis using normalizing transformation, Communications for Statistical Applications and Methods, 27, 501-510. https://doi.org/10.29220/CSAM.2020.27.5.501
  22. Kim N (2021). A Jarque-Bera type test for multivariate normality based on second-power skewness and kurtosis, Communications for Statistical Applications and Methods, 28, 463-475. https://doi.org/10.29220/CSAM.2021.28.5.463
  23. Kim N and Bickel PJ (2003). The limit distribution of a test statistic for bivariate normality, Statistica Sinica, 13, 327-349.
  24. Korkmaz S, Goksuluk D, and Zararsiz G (2014). MVN : An R package for assessing multivariate normality, The R Journal, 6, 151-162. https://doi.org/10.32614/RJ-2014-031
  25. Looney SW (1995). How to use tests for univariate normality to assess multivariate normality, The American Statistician, 49, 64-70. https://doi.org/10.1080/00031305.1995.10476117
  26. Malkovich JF and Afifi AA (1973). On tests for multivariate normality, Journal of the American Statistical Association, 68, 176-179. https://doi.org/10.1080/01621459.1973.10481358
  27. Mardia KV (1970). Measures of multivariate skewness and kurtosis with applications, Biometrika, 57, 519-530. https://doi.org/10.1093/biomet/57.3.519
  28. Mardia KV (1974). Applications of some measures of multivariate skewness and kurtosis for testing normality and robustness studies, Sankhya A, 36, 115-128.
  29. Mecklin CJ and Mundfrom DJ (2005). A Monte Carlo comparison of the type I and type II error rates of tests of multivariate normality, Journal of Statistical Computation and Simulation, 75, 93-107. https://doi.org/10.1080/0094965042000193233
  30. Romeu JL and Ozturk A (1993). A comparative study of goodness-of-fit tests for multivariate normality, Journal of Multivariate Analysis, 46, 309-334. https://doi.org/10.1006/jmva.1993.1063
  31. Roy SN (1953). On a heuristic method of test construction and its use in multivariate analysis, Annals of Mathematical Statistics, 24, 220-238. https://doi.org/10.1214/aoms/1177729029
  32. Shapiro SS and Wilk MB (1965). An analysis of variance test for normality (complete samples), Biometrika, 52, 591-611. https://doi.org/10.1093/biomet/52.3-4.591
  33. Small N (1980). Marginal skewness and kurtosis in testing multivariate normality, Journal of the Royal Statistical Society. Series C (Applied Statistics), 29, 85-87.
  34. Srivastava MS and Hui TK (1987). On assessing multivariate normality based on Shapiro-Wilk W statistic, Statistics & Probability Letters, 5, 15-18. https://doi.org/10.1016/0167-7152(87)90019-8
  35. Srivastava DK and Mudholkar GS (2003). Goodness-of-Fit tests for univariate and multivariate normal models, In R Khattree, and CR Rao (Eds), Handbook of Statistics 22: Statistics in Industry (pp. 869-906), Elsevier, North Holland.
  36. Thode Jr. HC (2002). Testing for Normality, Marcel Dekker, New York.
  37. Villasenor-Alva JA and Gonzalez-Estrada E (2009). A generalization of Shapiro-Wilk's test for multivariate normality, Communications in Statistics-Theory and Methods, 38, 1870-1883. https://doi.org/10.1080/03610920802474465
  38. Zhou M and Shao Y (2014). A powerful test for multivariate normality, Journal of Applied Statistics, 41, 351-363. https://doi.org/10.1080/02664763.2013.839637