Nonparametric analysis of covariance for censored data

Yunling Du, Michael G. Akritas, Ingrid Van Keilegom

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


The fully nonparametric model for nonlinear analysis of covariance, proposed in Akritas et al. (2000), is considered in the context of censored observations. Under this model, the distributions for each factor level combination and covariate value are not restricted to comply to any parametric or semiparametric model. The data can be continuous or ordinal categorical. The possibility of different shapes of covariate effect in different factor level combinations is also allowed. This generality is useful whenever modelling assumptions such as additive risks, proportional hazards or proportional odds appear suspect. Test statistics are obtained for the nonparametric hypotheses of no main effect and of no interaction effect which adjusts for the presence of a covariate. They are quadratic forms based on averages over the covariate values of Beran estimators of the conditional distribution of the survival time given each covariate value. The derivation of the asymptotic ξ2 distribution of the test statistics uses a recently-obtained asymptotic representation of the Beran estimator as average of independent random variables. A real-data set is analysed and results of simulation studies are reported.

Original languageEnglish (US)
Pages (from-to)269-287
Number of pages19
Issue number2
StatePublished - Jun 2003

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • General Mathematics
  • Agricultural and Biological Sciences (miscellaneous)
  • General Agricultural and Biological Sciences
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


Dive into the research topics of 'Nonparametric analysis of covariance for censored data'. Together they form a unique fingerprint.

Cite this