Variable selection for bivariate interval-censored failure time data under linear transformation models
Author:
Affiliation:
1. Center for Applied Statistical Research, School of Mathematics , Jilin University , Changchun 130012 , China
2. Department of Statistics , University of Missouri , Columbia , MO , 65211 , USA
Abstract
Publisher
Walter de Gruyter GmbH
Subject
Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability
Link
https://www.degruyter.com/document/doi/10.1515/ijb-2021-0031/pdf
Reference29 articles.
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2. Fan, J, Li, R. Variable selection via nonconcave penalized likelihood and its oracle property. J Am Stat Assoc 2001;96:1348–60. https://doi.org/10.1198/016214501753382273.
3. Liu, Z, Li, G. Efficient regularized regression with penalty for variable selection and network construction. Comput Math Methods Med 2016:3456153. https://doi.org/10.1155/2016/3456153.
4. Tibshirani, R. Regression shrinkage and selection via the lasso. Journel of the Royal Statistical Society 1996;58:267–88. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.
5. Zou, H. The adaptive lasso and its oracle properties. J Am Stat Assoc 2006;101:1418–29. https://doi.org/10.1198/016214506000000735.
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2. Overview of Recent Advances on the Analysis of Interval-Censored Failure Time Data;Emerging Topics in Modeling Interval-Censored Survival Data;2022
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