Particle swarm optimization-based variable selection in Poisson regression analysis via information complexity-type criteria
- 8 December 2017
- journal article
- research article
- Published by Taylor & Francis Ltd in Communications in Statistics - Theory and Methods
- Vol. 47 (21), 5298-5306
- https://doi.org/10.1080/03610926.2017.1390129
Abstract
Modeling of count responses is widely performed via Poisson regression models. This paper covers the problem of variable selection in Poisson regression analysis. The basic emphasis of this paper is to present the usefulness of information complexity-based criteria for Poisson regression. Particle swarm optimization (PSO) algorithm was adopted to minimize the information criteria. A real dataset example and two simulation studies were conducted for highly collinear and lowly correlated datasets. Results demonstrate the capability of information complexity-type criteria. According to the results, information complexity-type criteria can be effectively used instead of classical criteria in count data modeling via the PSO algorithm.Keywords
This publication has 19 references indexed in Scilit:
- BNC-PSO: structure learning of Bayesian networks by Particle Swarm OptimizationInformation Sciences, 2016
- Modeling Count DataPublished by Cambridge University Press (CUP) ,2014
- Particle swarm optimizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- A stepwise regression algorithm for high-dimensional variable selectionJournal of Statistical Computation and Simulation, 2014
- Model selection using information criteria under a new estimation method: least squares ratioJournal of Applied Statistics, 2011
- glmulti: AnRPackage for Easy Automated Model Selection with (Generalized) Linear ModelsJournal of Statistical Software, 2010
- Akaike's Information Criterion and Recent Developments in Information ComplexityJournal of Mathematical Psychology, 2000
- Tabu search model selection in multiple regression analysisCommunications in Statistics - Simulation and Computation, 1999
- Regression Analysis of Count DataPublished by Cambridge University Press (CUP) ,1998
- Maximum likelihood identification of Gaussian autoregressive moving average modelsBiometrika, 1973