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

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 s...

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Bibliographic Details
Published in:Communications in statistics. Theory and methods 2018-11, Vol.47 (21), p.5298-5306
Main Authors: Koç, Haydar, Dünder, Emre, Gümüştekin, Serpil, Koç, Tuba, Cengiz, Mehmet Ali
Format: Article
Language:English
Subjects:
Algorithms
Complexity
Computer simulation
Criteria
Modelling
Particle swarm optimization
Poisson density functions
Poisson regression
Regression analysis
Regression models
Statistical analysis
Variable selection
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Summary: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.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2017.1390129