The kurtosis and skewness of distributions are important measures that can describe the shape of a distribution, and there have been many results for symmetric distributions, but there are still many difficulties and challenges in the characterization of skew distributions. Based on the results of Mardia's and Song's kurtosis measures of elliptical distributions obtained by Zografos [
Citation: Xueying Yu, Chuancun Yin. Some results on multivariate measures of elliptical and skew-elliptical distributions: higher-order moments, skewness and kurtosis[J]. AIMS Mathematics, 2023, 8(3): 7346-7376. doi: 10.3934/math.2023370
The kurtosis and skewness of distributions are important measures that can describe the shape of a distribution, and there have been many results for symmetric distributions, but there are still many difficulties and challenges in the characterization of skew distributions. Based on the results of Mardia's and Song's kurtosis measures of elliptical distributions obtained by Zografos [
[1] | K. Zografos, On Mardia's and Song's measures of kurtosis in elliptical distributions, J. Multivariate Anal., 99 (2008), 858–879. https://doi.org/10.1016/j.jmva.2007.05.001 doi: 10.1016/j.jmva.2007.05.001 |
[2] | B. C. Arnold, R. A. Groeneveld, Measuring skewness with respect to the mode, The American Statisticia, 49 (1995), 34–38. https://doi.org/10.1080/00031305.1995.10476109 doi: 10.1080/00031305.1995.10476109 |
[3] | J. Av$\acute{e}$rous, M. Meste, Skewness for multivariate distributions: two approaches, Ann. Stat., 25 (1997), 1984–1997. https://doi.org/10.1214/aos/1069362381 doi: 10.1214/aos/1069362381 |
[4] | M. Ekstr$\ddot{o}$m, S. R. Jammalamadaka, A general measure of skewness, Stat. Probabil. Lett., 82 (2012), 1559–1568. https://doi.org/10.1016/j.spl.2012.04.011 doi: 10.1016/j.spl.2012.04.011 |
[5] | K. V. Mardia, Measures of multivariate skewness and kurtosis with applications, Biometrika, 57 (1970), 519–530. https://doi.org/10.1093/biomet/57.3.519 doi: 10.1093/biomet/57.3.519 |
[6] | S. N. Roy, On a heuristic method of test construction and its use in multivariate analysis, Ann. Math. Statist, 24 (1953), 220–238. https://doi.org/10.1214/aoms/1177729029 doi: 10.1214/aoms/1177729029 |
[7] | J. F.Malkovich, A. A. Afifi, On tests for multivariate normality, J. Am. Stat. Assoc., 68 (1973), 176–179. https://doi.org/10.1080/01621459.1973.10481358 doi: 10.1080/01621459.1973.10481358 |
[8] | K. S. Song, R$\acute{e}$nyi information, loglikelihood and an intrinsic distribution measure, J. Stat. Plann. Infer., 93 (2001), 51–69. https://doi.org/10.1016/S0378-3758(00)00169-5 doi: 10.1016/S0378-3758(00)00169-5 |
[9] | N. Henze, On Mardia's kurtosis test for multivariate normality, Commun. Stat.-Theor. M., 23 (1994), 1031–1045. https://doi.org/10.1080/03610929408831303 doi: 10.1080/03610929408831303 |
[10] | A. Azzalini, A. D. Valle, The multivariate skew-normal distribution, Biometrika, 83 (1996), 715–726. https://doi.org/10.1093/biomet/83.4.715 doi: 10.1093/biomet/83.4.715 |
[11] | A. R$\acute{e}$nyi, On measures of entropy and information, Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1 (1961), 547–561. |
[12] | M. G. Genton, X. Liu, Moments of skew-normal random vectors and their quadratic forms, Stat. Probabil. Lett., 51 (2003), 319–325. https://doi.org/10.1016/S0167-7152(00)00164-4 doi: 10.1016/S0167-7152(00)00164-4 |
[13] | T. Kollo, Multivariate skewness and kurtosis measures with an application in ICA, J. Multivariate Anal., 99 (2008), 2328–2338. https://doi.org/10.1016/j.jmva.2008.02.033 doi: 10.1016/j.jmva.2008.02.033 |
[14] | N. Balakrishnan, B. Scarpa, Multivariate measures of skewness for the skew-normal distribution, J. Multivariate Anal., 104 (2012), 73–87. https://doi.org/10.1016/j.jmva.2011.06.017 doi: 10.1016/j.jmva.2011.06.017 |
[15] | W. Tian, C. Wang, T.wang, The multivariate extended skew normal distribution and its quadratic forms, in Huynh, VN., Kreinovich, V., Sriboonchitta, S. (eds) Causal Inference in Econometrics, Springer, Cham, 2016. |
[16] | H. M. Kim, C. Kim, Moments of scale mixtures of skew-normal distributions and their quadratic forms, Commun. Stat.-Theor. M., 46 (2017), 1117–1126. https://doi.org/10.1080/03610926.2015.1011339 doi: 10.1080/03610926.2015.1011339 |
[17] | S. R. Jammalamadaka, E. Taufer, G. H. Terdik, On multivariate skewness and kurtosis, Sankhya A, 83 (2021), 607–644. https://doi.org/10.1007/s13171-020-00211-6 doi: 10.1007/s13171-020-00211-6 |
[18] | M. Abdi, M. Madadi, N. Balakrishnan, A. Jamalizadeh, Family of mean-mixtures of multivariate normal distributions: Properties, inference and assessment of multivariate skewness, J. Multivariate Anal., 181 (2021), 104679. https://doi.org/10.1016/j.jmva.2020.104679 doi: 10.1016/j.jmva.2020.104679 |
[19] | R. B. Arellano-Valle, A. Azzalini, A formulation for continuous mixtures of multivariate normal distributions, J. Multivariate Anal., 185 (2021), 104780. https://doi.org/10.1016/j.jmva.2021.104780 doi: 10.1016/j.jmva.2021.104780 |
[20] | M. Amiri, N. Balakrishnan, Hessian and increasing-Hessian orderings of scale-shape mixtures of multivariate skew-normal distributions and applications, J. Comput. Appl. Math., 402 (2022), 113801. https://doi.org/10.1016/j.cam.2021.113801 doi: 10.1016/j.cam.2021.113801 |
[21] | B. Zuo, C. Yin, Tail conditional expectations for generalized skew-elliptical distributions, Probability in the Engineering and Informational Sciences, 36 (2022), 500–513. https://doi.org/10.1017/S0269964820000674 doi: 10.1017/S0269964820000674 |
[22] | M. E. Johnson, Multivariate Statistical Simulation, John Wiley & Sons, New York, 1987. https://doi.org/10.1002/9781118150740 |
[23] | K. T. Fang, Y. T. Zhang, Generalized Multivariate Analysis, Science Press & Springer, Beijing & Berlin, 1990. |
[24] | A. Azzalini, A class of distributions which includes the normal ones, Scand. J. Stat., 12 (1985), 171–178. |
[25] | M. D. Branco, D. K. Dey, A general class of multivariate skew-elliptical distributions, J. Multivariate Anal., 79 (2001), 99–113. https://doi.org/10.1006/jmva.2000.1960 doi: 10.1006/jmva.2000.1960 |
[26] | A. Azzalini, An overview on the progeny of the skew-normal family - A personal perspective, J. Multivariate Anal., 188 (2022), 104851. https://doi.org/10.1016/j.jmva.2021.104851 doi: 10.1016/j.jmva.2021.104851 |
[27] | S. X. Lee, G. J. McLachlan, An overview of skew distributions in model-based clustering, J. Multivariate Anal., 188 (2022), 104853. https://doi.org/10.1016/j.jmva.2021.104853 doi: 10.1016/j.jmva.2021.104853 |
[28] | A. Azzalini, A. Capitanio, The Skew-Normal and Related Families, IMS monographs, Cambridge University Press, Cambridge, 2014. https://doi.org/10.1017/CBO9781139248891 |
[29] | E. G$\acute{o}$mez, M. A. G$\acute{o}$mez-Villegas, J. M. Mar$\acute{\shortmid}$n, A survey on continuous elliptical vector distributions, Revista Matem$\acute{a}$tica Complutense, 16 (2003), 345–361. https://doi.org/10.5209/rev_REMA.2003.v16.n1.16889 doi: 10.5209/rev_REMA.2003.v16.n1.16889 |
[30] | G. Terdik, Multivariate Statistical Methods: Going Beyond the Linear, Springer Nature, 2021. https://doi.org/10.1007/978-3-030-81392-5 |
[31] | M. G. Genton, Skew-elliptical Distributions and Their Applications: A Journey Beyond Normality, Chapman & Hall/CRC Press, Boca Raton, 2004. |
[32] | J. R. Schott, Matrix Analysis for Statistics, John Wiley & Sons, New York, 1997. |
[33] | B. Zuo, C. Yin, Multivariate tail covariance for generalized skew-elliptical distributions, J. Comput. Appl. Math., 410 (2022), 114210. https://doi.org/10.1016/j.cam.2022.114210 doi: 10.1016/j.cam.2022.114210 |