Nonparametric Methods for Factorial Designs with Censored Data

Michael G. Akritas, Edgar Brunner

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Nonparametric hypotheses of no main effects, no simple effects, and no interaction effects are considered in the context of factorial designs with censored observations. To test these hypotheses, the asymptotic distribution of quadratic forms of rank statistics is derived using the methodology of martingales for counting processes. The weights used reduce to the usual logistic scores with uncensored data but are different from the weights commonly used for testing the equality of k samples. The formulation of all results includes tied observations. Approximations to the small-sample distributions of the test statistics are given. The performance of the tests is examined via simulation studies. The procedures are illustrated on a real dataset.

Original languageEnglish (US)
Pages (from-to)568-576
Number of pages9
JournalJournal of the American Statistical Association
Issue number438
StatePublished - Jun 1 1997

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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