Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Variable selection in Poisson HGLMs using h-likelihoood

  • Ha, Il Do (Department of Statistics, Pukyong National University) ;
  • Cho, Geon-Ho (Faculty of Medical Industry Convergence, Daegu Haany University)
  • Received : 2015.07.23
  • Accepted : 2015.09.17
  • Published : 2015.11.30
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Abstract

Selecting relevant variables for a statistical model is very important in regression analysis. Recently, variable selection methods using a penalized likelihood have been widely studied in various regression models. The main advantage of these methods is that they select important variables and estimate the regression coefficients of the covariates, simultaneously. In this paper, we propose a simple procedure based on a penalized h-likelihood (HL) for variable selection in Poisson hierarchical generalized linear models (HGLMs) for correlated count data. For this we consider three penalty functions (LASSO, SCAD and HL), and derive the corresponding variable-selection procedures. The proposed method is illustrated using a practical example.

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References

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