Nonparametric inference in factorial designs with censored data

Michael G. Akritas, Michael P. LaValley

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


A method is proposed for testing the hypotheses of no main effects and no interaction in factorial designs with several observations per cell. The method uses the fact that these hypotheses can be expressed in terms of a vector of contrasts. It is based on the observation that nonparametric estimation of these contrasts is no more difficult than estimation of the location difference in the two-sample problem. To implement the method with censored data, a new extension of the Hodges-Lehmann estimator is proposed. The estimator is simple to compute and its variance is easily evaluated. A simulation study examines the performance of the proposed estimation and testing method in the context of a two-by-two design, and a real data set from a three-way layout with heavy censoring is analyzed.

Original languageEnglish (US)
Pages (from-to)913-924
Number of pages12
Issue number3
StatePublished - 1996

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


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