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Variable selection in gamma regression models via artificial bee colony algorithm

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  • Emre Dunder
  • Serpil Gumustekin
  • Mehmet Ali Cengiz

Abstract

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.

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  • Emre Dunder & Serpil Gumustekin & Mehmet Ali Cengiz, 2018. "Variable selection in gamma regression models via artificial bee colony algorithm," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 8-16, January.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:8-16
    DOI: 10.1080/02664763.2016.1254730
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