A semiparametric approach for the nonparametric transformation survival model with multiple covariates

Xiao Song, Shuangge Ma, Jian Huang, Xiao Hua Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

24 Citations (Scopus)


The nonparametric transformation model makes no parametric assumptions on the forms of the transformation function and the error distribution. This model is appealing in its flexibility for modeling censored survival data. Current approaches for estimation of the regression parameters involve maximizing discontinuous objective functions, which are numerically infeasible to implement with multiple covariates. Based on the partial rank (PR) estimator (Khan and Tamer, 2004), we propose a smoothed PR estimator which maximizes a smooth approximation of the PR objective function. The estimator is shown to be asymptotically equivalent to the PR estimator but is much easier to compute when there are multiple covariates. We further propose using the weighted bootstrap, which is more stable than the usual sandwich technique with smoothing parameters, for estimating the standard error. The estimator is evaluated via simulation studies and illustrated with the Veterans Administration lung cancer data set.
Original languageEnglish
Pages (from-to)197-211
Number of pages15
Issue number2
Publication statusPublished - 1 Apr 2007
Externally publishedYes


  • Nonparametric transformation model
  • Partial rank estimator
  • Survival analysis
  • Weighted bootstrap

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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