Variable selection in gamma regression models via artificial bee colony algorithm

Dünder E., Gumustekin S., Cengiz M. A.

JOURNAL OF APPLIED STATISTICS, sa.1, ss.8-16, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1080/02664763.2016.1254730
  • Dergi Adı: JOURNAL OF APPLIED STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.8-16
  • Anahtar Kelimeler: artificial bee colony algorithm, gamma regression analysis, heuristic optimization, R-project, Variable selection
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

Variable selection is an important task in regression analysis. Performance of the statistical model highly depends on the determination of the subset of predictors. There are several methods to select most relevant variables to construct a good model. However in practice, the dependent variable may have positive continuous values and not normally distributed. In such situations, gamma distribution is more suitable than normal for building a regression model. This paper introduces an heuristic approach to perform variable selection using artificial bee colony optimization for gamma regression models. We evaluated the proposed method against with classical selection methods such as backward and stepwise. Both simulation studies and real data set examples proved the accuracy of our selection procedure.