Abstract
In this paper, we consider the problem of estimating the location and scale parameters of the skew normal distribution introduced by Azzalini. For this distribution, the classic maximum likelihood estimators(MLEs) do not take explicit forms. We approximate the likelihood equations and derive explicit estimators of the parameters. The bias and variance of the estimators are investigated and Monte Carlo simulation studies show that the estimators are as efficient as the classic MLEs. We demonstrate that the probability coverages of the pivotal quantities (for location and scale parameters) based on asymptotic normality are unsatisfactory, especially when the sample size is small. The use of unconditional simulated percentage points of these quantities is suggested. Finally, a numerical example is used to illustrate the proposed inference methods.
Similar content being viewed by others
References
B. Arnold and N. Balakrishnan, Relations, bounds and approximations for order statistics, 53 (2012), Springer Science and Business Media.
B. C. Arnold, R. J. Beaver, R. A. Groeneveld, and W. Q. Meeker, The non-truncated marginal of a truncated bivariate normal distribution, Psychometrika 58(3) (1993), 471–488.
A. Asgharzadeh, L. Esmaily, and S. Nadarajah, Approximate mles for the location and scale parameters of the skew logistic distribution, Statistical Papers, 54(2) (2013), 391–411.
A. Azzalini, A class of distributions which includes the normal ones, Scandinavian Journal of Statistics, (1985), 171–178.
A. Azzalini, The skew-normal distribution and related multivariate families, Scandinavian Journal of Statistics, (2005), 159–188.
A. Azzalini and A. Capitanio, Statistical applications of the multivariate skew normal distribution, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(3) (1999), 579–602.
A. Azzalini and A. Dalla Valle, The multivariate skew-normal distribution, Biometrika, 83(4) (1996), 715–726.
M. Chiogna, Some results on the scalar skew-normal distribution, Journal of the Italian Statistical Society, 7(1) (1998), 1–13.
T. S. Ferguson, A course in large sample theory, Vol. 49 (1996), Chapman and Hall London.
S. Ghosh and D. Dey, Estimation of the parameters of skew normal distribution by approximating the ratio of the normal density and distribution functions, Dissertations and Theses - Gradworks, (2010).
R. C. Gupta and N. Brown, Reliability studies of the skew-normal distribution and its application to a strength-stress model, Communications in Statistics-Theory and Methods, 30(11) (2001), 2427–2445.
N. Henze, A probabilistic representation of the’ skew-normal’ distribution, Scandinavian Journal of Statistics, (1986), 271–275.
A. Hossain and J. Beyene, Application of skew-normal distribution for detecting differential expression to Microrna data, Journal of Applied Statistics, 42(3) (2015), 477–491.
B. Liseo and N. Loperfido, A bayesian interpretation of the multivariate skew-normal distribution, Statistics & probability letters, 61(4) (2003), 395–401.
B. Liseo and N. Loperfido, Default bayesian analysis of the skew-normal distribution (2004).
B. Liseo and N. Loperfido, A note on reference priors for the scalar skew-normal distribution, Journal of Statistical Planning and Inference, 136(2) (2006), 373–389.
W. Ning and G. Ngunkeng, An empirical likelihood ratio based goodness-of-fit test for skew normality, Statistical Methods & Applications, 22(2) (2013), 209–226.
H. V. Roberts, Data analysis for managers with Minitab: Harry V. Roberts, Scientific Press, (1991).
N. Sartori, Bias prevention of maximum likelihood estimates for scalar skew normal and skew t distributions, Journal of Statistical Planning and Inference, 136(12) (2006), 4259–4275.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gui, W., Guo, L. Statistical Inference for the Location and Scale Parameters of the Skew Normal Distribution. Indian J Pure Appl Math 49, 633–650 (2018). https://doi.org/10.1007/s13226-018-0291-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13226-018-0291-6