Particle swarm optimization-based variable selection in Poisson regression analysis via information complexity-type criteria
Particle swarm optimization-based variable selection in Poisson regression analysis via information complexity-type criteria
复制标题
DOI：
10.1080/03610926.2017.1390129
复制
发表时间：
2018-01-01
影响因子：
0.8
通讯作者：
Cengiz, Mehmet Ali
中科院分区：
文献类型：
--
作者：
Koc, Haydar;Dunder, Emre;Cengiz, Mehmet Ali
计数响应的建模广泛地通过泊松回归模型来执行。本文讨论Poisson回归分析中的变量选择问题。本文的基本重点是提出基于信息复杂性的泊松回归准则的有用性。采用粒子群算法(PSO)最小化信息量准则。针对高度共线和低相关性的数据集进行了一个真实的数据集实例和两个仿真研究。结果证明了信息复杂性类型标准的能力。结果表明，在基于PSO算法的计数数据建模中，可以有效地使用信息复杂性准则来代替经典准则。
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.