EcoSta 2018: Registration
View Submission - EcoSta2018
Title: A constrained maximum likelihood estimation for skew normal mixtures Authors:  Libin Jin - Shanghai Lixin University of Accounting and Finance (China) [presenting]
Sung Nok Chiu - Hong Kong Baptist University (Hong Kong)
Lixing Zhu - Hong Kong Baptist University (Hong Kong)
Abstract: For a finite mixture of skew normal distributions, the maximum likelihood estimator is not well-defined because of the unboundedness of the likelihood function when scale parameters go to zero and the divergency of the skewness parameter estimates. To overcome these two problems simultaneously, we propose constrained maximum likelihood estimators under constraints on both the scale parameters and the skewness parameters. The proposed estimators are consistent and asymptotically efficient under relaxed constraints on the scale and skewness parameters. Numerical simulations show that in finite sample cases the proposed estimators outperform the ordinary maximum likelihood estimators. A real data set of Iris flowers is used to illustrate the success of the proposed approach.