References
- Batsidis, A., N. Martin, L. Pardo, and K. Zografos. 2013. A necessary power divergence type family tests of multivariate normality. Communications in Statistics — Simulation and Computation 42 (10):2253–2271. doi:10.1080/03610918.2012.697238.
- Bilodeau, M., and D. Brenner. 1999. Theory of multivariate statistics. New York: Springer-Verlag.
- Cardoso de Oliveira, I. R. C., and D. F. Ferreira. 2010. Multivariate extension of chi-squared univariate normality test. Journal of Statistical Computation and Simulation 80 (5):513–525. doi:10.1080/00949650902731377.
- Gnanadesikan, R., and J. R. Kettenring. 1972. Robust estimates, residuals, and outlier detection with multiresponse data. Biometrics 28:81–124. doi:10.2307/2528963.
- Hanusz, Z., and J. Tarasinska. 2012. New tests for multivariate normality based on Small's and Srivastava's graphical methods. Journal of Statistical Computation and Simulation 82 (12):1743–1752. doi:10.1080/00949655.2011.594051.
- Healy, M. J. R. 1968. Multivariate normal plotting. Applied Statistics 17 (2):157–161. doi:10.2307/2985678.
- Henze, N. 1994. On Mardia's kurtosis test for multivariate normality. Communications in Statistics - Theory and Methods 23:1031–1045. doi:10.1080/03610929408831303.
- Henze, N. 2002. Invariant tests for multivariate normality: A critical review. Statistical Papers 43:467–506. doi:10.1007/s00362-002-0119-6.
- Henze, N., and B. Zirkler. 1990. A class of invariant consistent tests for multivariate normality. Communications in Statistics — Theory and Methods 19:3595–3618. doi:10.1080/03610929008830400.
- Joenssen, D. W., and J. Vogel. 2014. A power study of goodness-of-fit tests for multivariate normality implemented in R. Journal of Statistical Computation and Simulation 84 (5):1055–1078. doi:10.1080/00949655.2012.739620.
- Koziol, J. A. 1986. A note on the asymptotic distribution of Mardia;s measure of multivariate kurtosis. Communications in Statistics — Theory and Methods 15 (5):1507–1513. doi:10.1080/03610928608829197.
- Liang, J., M. L. Fang, and P. S. Chang. 2009. A generalized Shapiro-Wilk W statistic for testing high–dimensional normality. Computational Statistics & Data Analysis 53:3883–3891. doi:10.1016/j.csda.2009.04.016.
- Madukaife, M. S., and F. C. Okafor. 2017. A powerful affine invariant test for multivariate normality based on interpoint distances of the principal components. Communications in Statistics - Simulation and Computation doi:10.1080/03610918.2017.1309667.
- Mardia, K. V. 1970. Measures of multivariate skewness and kurtosis with applications. Biometrika 573:519–530. doi:10.1093/biomet/57.3.519.
- Mardia, K. V. 1974. Applications of some measures of multivariate skewness and kurtosis for testing normality and robustness studies. Sankhya 36:115–128.
- Mecklin, C. J., and D. J. Mundfrom. 2004. An appraisal and bibliography of tests for multivariate normality. International Statistical Review 72 (1):123–138. doi:10.1111/j.1751-5823.2004.tb00228.x.
- Romeu, J. L., and A. Ozturk. 1993. A comparative study of goodness-of-fit tests for multivariate normality. Journal of Multivariate Analysis 46:309–334. doi:10.1006/jmva.1993.1063.
- Royston, J. P. 1982a. An extension of Shapiro and Wilk's W test for normality to large samples. Applied Statistics 31 (2):115–124. doi:10.2307/2347973.
- Royston, J. P. 1982b. Algorithm AS 181: The W test for normality. Applied Statistics 31 (2):176–180. doi:10.2307/2347986.
- Royston, J. P. 1983. Some techniques for assessing multivariate normality based on the Shapiro-Wilk W. Applied Statistics 32 (2):121–133. doi:10.2307/2347291.
- Scrucca, L. 2000. Assessing multivariate normality through interactive dynamic graphics. Quaderni di Statistica, 2:221–240.
- Shapiro, S. S., and M. B. Wilk. 1965. An analysis of variance test for normality (complete samples). Biometrika 52 (3 and 4):591–611. doi:10.1093/biomet/52.3-4.591.
- Small, N. J. H. 1978. Plotting squared radii. Biometrika. 65 (3):657–658. doi:10.1093/biomet/65.3.657.
- Szekely, G. J., and M. L. Rizzo 2005. A new test for multivariate normality. Journal of Multivariate Analysis 93:58–80. doi:10.1016/j.jmva.2003.12.002.
- Tenreiro, C. 2015. A new test for multivariate normality by combining extreme and non-extreme BHEP tests. doi:10.1080/03610918.2015.1011334.
- Thode, H. C. 2002. Testing for Normality. New York: Marcel Dekker.
- Thulin, M. 2014. Tests for multivariate normality based on canonical correlations. Statistical Methods & Applications doi:10.1007/s10260-013-0252-5.
- Voinov, V., N. Pya, R. Makarov, and Y. Voinov 2016. New invariant and consistent chi-squared type goodness-of-fit tests for multivariate normality and a related comparative simulation study. Communications in Statistics — Theory and Methods 45 (11):3249–3263. doi:10.1080/03610926.2014.901370.
- von Eye, A. 2005. Comparing tests of multinormality—A Monte Carlo study. InterStat http://interstat.statjournals.net/.
- Yamada, T., M. M. Romer, and D.StP. Richards. 2015. Kurtosis tests for multivariate normality with monotone incomplete data. Test doi:10.1007/s11749-014-0423-1.
- Zhou, M., and Y. Shao. 2014. A powerful test for multivariate normality. Journal of Applied Statistics 41:1–13. doi:10.1080/02664763.2013.839637.