Proceedings of the 2018 International Conference on Computer Modeling, Simulation and Algorithm (CMSA 2018)

Estimation and Application of Skew-normal Data for Generalized Linear Regression

Authors
Wenjun Lyu, Zhaoqing Feng
Corresponding Author
Wenjun Lyu
Available Online April 2018.
DOI
https://doi.org/10.2991/cmsa-18.2018.48How to use a DOI?
Keywords
skew-normal distributions; generalized linear models; EM-algorithm
Abstract

Generalized linear models are generally applied in statistical researches. Since a lot of real data reveal nonnormality especially skew-normality, new assumption is proposed that error terms follow skew-normal distribution to increase the adaptability of GLMs, which forms GLMSNs. To estimate the parameters in the linear part in models, penalized expectation maximization algorithm is extended. This paper focuses on the combination of skew-normal data and GLMs to get more robust results. Several applications and empirical analyses are given to fit GLMSNs and models selection is presented by Bayesian information criterion.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2018 International Conference on Computer Modeling, Simulation and Algorithm (CMSA 2018)
Series
Advances in Intelligent Systems Research
Publication Date
April 2018
ISBN
10.2991/cmsa-18.2018.48
ISSN
1951-6851
DOI
https://doi.org/10.2991/cmsa-18.2018.48How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Wenjun Lyu
AU  - Zhaoqing Feng
PY  - 2018/04
DA  - 2018/04
TI  - Estimation and Application of Skew-normal Data for Generalized Linear Regression
BT  - Proceedings of the 2018 International Conference on Computer Modeling, Simulation and Algorithm (CMSA 2018)
PB  - Atlantis Press
SP  - 208
EP  - 211
SN  - 1951-6851
UR  - https://doi.org/10.2991/cmsa-18.2018.48
DO  - https://doi.org/10.2991/cmsa-18.2018.48
ID  - Lyu2018/04
ER  -