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Variable selection in Poisson HGLMs using h-likelihoood
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 Title & Authors
Variable selection in Poisson HGLMs using h-likelihoood
Ha, Il Do; Cho, Geon-Ho;
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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.
 Keywords
LASSO;penalized h-likelihood;Poisson HGLMs;SCAD;variable selection;
 Language
English
 Cited by
1.
Joint HGLM approach for repeated measures and survival data, Journal of the Korean Data and Information Science Society, 2016, 27, 4, 1083  crossref(new windwow)
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