Bayesian Subset Selection for Inverse Gauss Regression Models

DOI

10.2991/ammsa-18.2018.37How to use a DOI?

Keywords

Bayesian subset selection; Gibbs sampler; Metropolis-Hastings algorithm; Inverse Gauss regression models

Abstract

Inspired by the idea of Kuo and Mallick, Bayesian subset selection for inverse Gauss regression models is studied by Gibbs sampler and Metropolis-Hastings algorithm in this paper. Simulation study and the aerobic fitness data example are employed to demonstrate the proposed methodology.

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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TY  - CONF
AU  - Yuanying Zhao
AU  - Dengke Xu
AU  - Liangqiong Jin
AU  - Qingqiong Jiang
PY  - 2018/05
DA  - 2018/05
TI  - Bayesian Subset Selection for Inverse Gauss Regression Models
BT  - Proceedings of the 2018 2nd International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2018)
PB  - Atlantis Press
SP  - 185
EP  - 189
SN  - 1951-6851
UR  - https://doi.org/10.2991/ammsa-18.2018.37
DO  - 10.2991/ammsa-18.2018.37
ID  - Zhao2018/05
ER  -