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

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  • Haydar Koç
  • Emre Dünder
  • Serpil Gümüştekin
  • Tuba Koç
  • Mehmet Ali Cengiz

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.

Suggested Citation

  • Haydar Koç & Emre Dünder & Serpil Gümüştekin & Tuba Koç & Mehmet Ali Cengiz, 2018. "Particle swarm optimization-based variable selection in Poisson regression analysis via information complexity-type criteria," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(21), pages 5298-5306, November.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:21:p:5298-5306
    DOI: 10.1080/03610926.2017.1390129
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    1. Yunus Emre Ayözen & Hakan İnaç & Abdulkadir Atalan & Cem Çağrı Dönmez, 2022. "E-Scooter Micro-Mobility Application for Postal Service: The Case of Turkey for Energy, Environment, and Economy Perspectives," Energies, MDPI, vol. 15(20), pages 1-22, October.

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