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• W2396538508 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." @default.
• W2396538508 created "2016-06-24" @default.
• W2396538508 creator A5004953843 @default.
• W2396538508 creator A5070406591 @default.
• W2396538508 date "2015-11-30" @default.
• W2396538508 modified "2023-09-24" @default.
• W2396538508 title "Variable selection in Poisson HGLMs using h-likelihoood" @default.
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• W2396538508 doi "https://doi.org/10.7465/jkdi.2015.26.6.1513" @default.
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