Skip to main content

Variable selection in semiparametric linear regression with censored data

Buy Article:

$43.00 plus tax (Refund Policy)

Summary. 

We describe two procedures for selecting variables in the semiparametric linear regression model for censored data. One procedure penalizes a vector of estimating equations and simultaneously estimates regression coefficients and selects submodels. A second procedure controls systematically the proportion of unimportant variables through forward selection and the addition of pseudorandom variables. We explore both rank-based statistics and Buckley–James statistics in the setting proposed and evaluate the performance of all methods through extensive simulation studies and one real data set.
No References
No Citations
No Supplementary Data
No Data/Media
No Metrics

Keywords: False selection rate; Hard thresholding; Non-smooth estimating function; Rank regression; Soft thresholding; Survival analysis

Document Type: Research Article

Affiliations: Emory University, Atlanta, USA

Publication date: 2008-04-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more