References
- Azzalini, A. 1985. A class of distribution which includes the normal ones. Scandinavian Journal of Statistics 12:171–78.
- Azzalini, A., and A. Capitanio. 2003. Distributions generated by perturbation of symmetry with emphasis on a multivariate skew-t distribution, Journal of the Royal Statistical Society B 65:367–89. doi:10.1111/1467-9868.00391.
- Cabras, S., and M. E. Castellanos. 2005. Comparing the skewness in two populations using bayesian inference on skew normal and skew t models. Working Paper, Dep. Stat., Rey Juan Carlos University, http://bayes.escet.urjc.es/publicaciones/wp05-11.pdf.
- Doane, D. P., and L. E. Seward. 2011. Measuring skewness: A forgotten statistic? Journal of Statistics Education 19:1–18. doi:10.1080/10691898.2011.11889611.
- Ferguson, T. S. 1996. A course in large sample theory. Chapman & Hall.
- Haghbin, H., M. R. Mahmoudi, and Z. Shishebor. 2011. Large sample inference on the ratio of two independent binomial proportions. Journal of Mathematical Extension 5 (1):87–95.
- Mahmoudi, M. R., and M. Mahmoodi. 2013a. Inferrence on the ratio of variances of two independent populations. Journal of Mathematical Extension 7 (2):83–91.
- Mahmoudi, M. R., and M. Mahmoodi. 2013b. Inferrence on the Ratio of Correlations of Two Independent Populations. Journal of Mathematical Extension 7 (4):71–82.
- Mahmoudi, M. R., M. Mahmoudi, and E. Nahavandi. 2016. Testing the difference between two independent regression models. Communications in Statistics — Theory and Methods 45 (21):6284–89. doi:10.1080/03610926.2014.960584.
- Mahmoudi, M. R., J. Behboodian, and M. Maleki. 2017a. Large sample inference about the ratio of means in two independent populations. Journal of Statistical Theory and Applications 16(3):366–74. doi:10.2991/jsta.2017.16.3.8.
- Mahmouudi, M. R., M. Maleki, and A. Pak. 2017b. Testing the difference between two independent time series models. Iranian Journal of Science and Technology: Sciences 41 (3):665–69. doi:10.1007/s40995-017-0288-8.
- Sokal, R. R., and F. J. Rohlf. 1994. Biometry: The principles and practice of statistics in biological research. 3rd Edition, San Francisco, CA: Freeman.