Particle swarm optimization-based variable selection in Poisson regression analysis via information complexity-type criteria

Koc H., Dünder E., Gumustekin S., Koc T., Cengiz M. A.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, vol.47, no.21, pp.5298-5306, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 47 Issue: 21
  • Publication Date: 2018
  • Doi Number: 10.1080/03610926.2017.1390129
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.5298-5306
  • Keywords: Poisson regression, Variable selection, Particle swarm optimization, MODEL SELECTION, SUBSET-SELECTION, TABU SEARCH
  • Ondokuz Mayıs University Affiliated: Yes


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.