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Variable selection for recurrent event data with broken adaptive ridge regression
Author(s) -
Zhao Hui,
Sun Dayu,
Li Gang,
Sun Jianguo
Publication year - 2018
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11459
Subject(s) - oracle , covariate , computer science , event (particle physics) , cluster analysis , variable (mathematics) , feature selection , regression , regression analysis , selection (genetic algorithm) , data mining , artificial intelligence , econometrics , machine learning , statistics , mathematics , physics , software engineering , quantum mechanics , mathematical analysis
Recurrent event data occur in many areas such as medical studies and social sciences and a great deal of literature has been established for their analysis. On the other hand, only limited research exists on the variable selection for recurrent event data, and the existing methods can be seen as direct generalizations of the available penalized procedures for linear models and may not perform as well as expected. This article discusses simultaneous parameter estimation and variable selection and presents a new method with a new penalty function, which will be referred to as the broken adaptive ridge regression approach. In addition to the establishment of the oracle property, we also show that the proposed method has the clustering or grouping effect when covariates are highly correlated. Furthermore, a numerical study is performed and indicates that the method works well for practical situations and can outperform existing methods. An application is provided. The Canadian Journal of Statistics 46: 416–428; 2018 © 2018 Statistical Society of Canada