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http://dx.doi.org/10.5351/KJAS.2015.28.5.965

Variable Selection in Frailty Models using FrailtyHL R Package: Breast Cancer Survival Data  

Kim, Bohyeon (Department of Statistics, Pukyong National University)
Ha, Il Do (Department of Statistics, Pukyong National University)
Noh, Maengseok (Department of Statistics, Pukyong National University)
Na, Myung Hwan (Department of Statistics, Chonnam National University)
Song, Ho-Chun (Department of Nuclear Medicine, Chonnam National University Hospital)
Kim, Jahae (Department of Nuclear Medicine, Chonnam National University Hospital)
Publication Information
The Korean Journal of Applied Statistics / v.28, no.5, 2015 , pp. 965-976 More about this Journal
Abstract
Determining relevant variables for a regression model is important in regression analysis. Recently, a variable selection methods using a penalized likelihood with various penalty functions (e.g. LASSO and SCAD) have been widely studied in simple statistical models such as linear models and generalized linear models. The advantage of these methods is that they select important variables and estimate regression coefficients, simultaneously; therefore, they delete insignificant variables by estimating their coefficients as zero. We study how to select proper variables based on penalized hierarchical likelihood (HL) in semi-parametric frailty models that allow three penalty functions, LASSO, SCAD and HL. For the variable selection we develop a new function in the "frailtyHL" R package. Our methods are illustrated with breast cancer survival data from the Medical Center at Chonnam National University in Korea. We compare the results from three variable-selection methods and discuss advantages and disadvantages.
Keywords
frailty models; H-likelihood; LASSO; SCAD; Variable selection;
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