- Volume 28 Issue 5
DOI QR Code
Variable Selection in Frailty Models using FrailtyHL R Package: Breast Cancer Survival Data
frailtyHL 통계패키지를 이용한 프레일티 모형의 변수선택: 유방암 생존자료
- 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)
- 김보현 (부경대학교 통계학과) ;
- 하일도 (부경대학교 통계학과) ;
- 노맹석 (부경대학교 통계학과) ;
- 나명환 (전남대학교 통계학과) ;
- 송호천 (전남대학교병원 핵의학과) ;
- 김자혜 (전남대학교병원 핵의학과)
- Received : 2015.07.27
- Accepted : 2015.08.06
- Published : 2015.10.31
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.
Supported by : 한국연구재단
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