A group bridge approach for component selection in nonparametric accelerated failure time additive regression model

Longlong Huang, Karen Kopciuk and Xuewen Lu

Communications in Statistics - Theory and Methods, 2021, vol. 50, issue 6, 1477-1501

Abstract: We study a nonparametric accelerated failure time (AFT) additive regression model whose covariates have nonparametric effects on the censored survival time. The proposed model is more flexible than the linear AFT model and can be used to perform dimension reduction and model building. Specifically, it can be used to discover the functional forms of all the covariates, whether a function is a zero or nonzero component; if it is a nonzero component, whether it is linear or nonlinear. First, we treat all the components as unknown nonlinear functions. B-splines are used to model these nonparametric components. A group bridge penalized variable selection approach based on the inverse probability-of-censoring weighted least squares is developed to select important nonparametric components and discover their functional forms simultaneously. Meanwhile, we compare the group bridge and group LASSO methods. The simulation results demonstrate that the group bridge method provides more accurate estimation and better selection performance than the group LASSO method, and the proposed method has satisfactory performance even with relatively high censoring rates. Two real data analyses are used to illustrate the application of the proposed method to censored survival data.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2019.1651861 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:50:y:2021:i:6:p:1477-1501

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2019.1651861

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().