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Mathematical Problems in Engineering
Volume 2014, Article ID 824816, 7 pages
http://dx.doi.org/10.1155/2014/824816
Research Article

Penalized Maximum Likelihood Method to a Class of Skewness Data Analysis

1School of Science, Huzhou Teachers College, Huzhou 313000, China
2School of Management, Chongqing Jiaotong University, Chongqing 400074, China

Received 3 August 2014; Accepted 14 September 2014; Published 29 September 2014

Academic Editor: Quanxin Zhu

Copyright © 2014 Xuedong Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

An extension of some standard likelihood and variable selection criteria based on procedures of linear regression models under the skew-normal distribution or the skew- distribution is developed. This novel class of models provides a useful generalization of symmetrical linear regression models, since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions. A generalized expectation-maximization algorithm is developed for computing the penalized estimator. Efficacy of the proposed methodology and algorithm is demonstrated by simulated data.