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Testing Multivariate Normality Based on EDF Statistics

EDF 통계량을 이용한 다변량 정규성검정

  • Published : 2006.07.01

Abstract

We generalize the $Cram{\acute{e}}r$-von Mises Statistic to test multivariate normality using Roy's union-intersection principle. We show the limit distribution of the suggested statistic is representable as the integral of a suitable Gaussian process. We also consider the computational aspects of the proposed statistic. Power performance is assessed in a Monte Carlo study.

EDF에 근거한 $Cram{\acute{e}}r$-von Mises 통계량을 합교원리를 이용하여 다변량으로 일반화한다. 그리고 제안된 통계량의 귀무가설에서의 극한분포를 적절한 공분산 함수를 가진 가우스 과정의 적분의 형태로 표현하고 통계량의 근사적인 계산방법을 고려한다. 또한 실제 자료에 제안된 통계량을 적용해보고 여러가지 대립가설에서의 검정력을 유사한 통계량과 비교해 본다.

Keywords

References

  1. 김남현 (2004a). 다변량 정규성검정을 위한 근사 Shapiro-Wilk 통계량의 일반화, <응용통계연구>, 17, 35-47
  2. 김남현 (2004b). 정규성 검정을 위한 다변량 왜도와 첨도의 이용에 대한 고찰, <응용통계연구>, 17, 507-518
  3. Billingsley, P. (1986). Probability and Measure, Wiley, New York
  4. Csorgo, S. (1989). Consistency of some tests for multivariate normality, Metrika, 36, 107-116 https://doi.org/10.1007/BF02614082
  5. Darling, D. A. (1955). The Cramer-Smirnov test in the parametric case, Annals of Mathematical statistics, 26, 1-20 https://doi.org/10.1214/aoms/1177728589
  6. Dudley, R. M. (1978). Central limit theorems for empirical measures, The Annals of probability, 6, 899-929 https://doi.org/10.1214/aop/1176995384
  7. Durbin, J. (1973). Weak convergence of the sample distribution function when parameters are estimated, The Annals of Statistics, 1, 279-290 https://doi.org/10.1214/aos/1176342365
  8. Fang, K. -T. and Wang, Y. (1993). Number-theoretic Methods in statistics, Chapman & Hall, London
  9. Fattorini, L. (1986). Remarks on the use of the Shapiro-Wilk statistic for testing multivariate normality, Statistica, 46, 209-217
  10. Finney, R. L. and Thomas, Jr. G. B. (1994). Calculus, Add-son-Wesley, New York
  11. Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems, Annals of Eugenics, VII, 179-188
  12. Gnanadesikan, R. (1977). Methods for statistical data analysis of multivariate observations, Wiley, New York
  13. Gnanadesikan, R. and Kettenring, J. R. (1972). Robust estimates, residuals, and outlier detection with multiresponse data. Biometrics, 28, 81-124 https://doi.org/10.2307/2528963
  14. Henze, N. and Zirkler, H. (1990). A class of invariant and consistent tests for multivariate normality, Communications in Statistics - Theory and Methods, 19, 3595-3617 https://doi.org/10.1080/03610929008830400
  15. Kendall, M. G. (1975). Multivariate Analysis, Griffin, London
  16. Kim, N. (2005). The limit distribution of an invariant test statistic for multivariate normality, The Korean Communications in Statistics, 12, 71-86 https://doi.org/10.5351/CKSS.2005.12.1.071
  17. Kim, N. and Bickel, P. J. (2003). The limit distribution of a test statistic for bivariate normality, Statistica Sinica, 13, 327-349
  18. Koziol, J. A. (1982). A class of invariant procedures for assessing multivariate normality, Biometrika, 69, 423-427 https://doi.org/10.1093/biomet/69.2.423
  19. Malkovich, J. F. and Afifi, A. A. (1973). On tests for multivariate normality, Journal of the American Statistical Association, 68, 176-179 https://doi.org/10.2307/2284163
  20. Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57, 519-530 https://doi.org/10.1093/biomet/57.3.519
  21. Mardia, K. V. (1974). Applications of some measures of multivariate skewness and kurtosis for testing normality and robustness studies. Sankhya A, 36, 115-128
  22. Massart, P. (1989). Strong approximation for multivariate empirical and related processes, via KMT constructions, The Annals of Probability, 17, 266-291 https://doi.org/10.1214/aop/1176991508
  23. Roy, S. N. (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
  24. Royston, J. P. (1983). Some techniques for assessing multivariate normality based on the Shapiro-Wilk W, Applied statistics, 32, 121-133 https://doi.org/10.2307/2347291
  25. Shorack, G. R. and Wellner, J. A. (1986). Empirical processes with applications to statistics, Wiley, New York
  26. Singh, A. (1993). Omnibus robust procedures for assessment of multivariate normality and detection of multivariate outliers. In : Multivariate Environment Statistics (G.P. Patil and C. R. Rao, eds.) North-Holland, Amsterdam, 445-488
  27. Small, N. J. H. (1980). Marginal skewness and kurtosis in testing multivariate normality, Applied Statistics, 29, 85-87 https://doi.org/10.2307/2346414
  28. Sukhatme, S. (1972). Predholm determinant of a positive definite kernel of a special type and its applications, Annals of Mathematical Statistics, 43, 1914-1926 https://doi.org/10.1214/aoms/1177690862
  29. Zhu, L. -X., Fang, K.-T. and Bhatti, M.I. (1997). On estimated projection pursuit-type Cramer-von Mises statistics, Journal of Multivariate Analysis, 63, 1-14 https://doi.org/10.1006/jmva.1997.1673