Penalized Maximum Likelihood Method to a Class of Skewness Data Analysis
Joint Authors
Chen, Xuedong
Zeng, Qianying
Song, Qiankun
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-09-28
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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- t 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 l 1 penalized estimator.
Efficacy of the proposed methodology and algorithm is demonstrated by simulated data.
American Psychological Association (APA)
Chen, Xuedong& Zeng, Qianying& Song, Qiankun. 2014. Penalized Maximum Likelihood Method to a Class of Skewness Data Analysis. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1046489
Modern Language Association (MLA)
Chen, Xuedong…[et al.]. Penalized Maximum Likelihood Method to a Class of Skewness Data Analysis. Mathematical Problems in Engineering No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-1046489
American Medical Association (AMA)
Chen, Xuedong& Zeng, Qianying& Song, Qiankun. Penalized Maximum Likelihood Method to a Class of Skewness Data Analysis. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1046489
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1046489