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Efficient rank tests for semiparametric competing risk models

Author

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  • Jan Beyersmann
  • Arnold Janssen
  • Claus-Dieter Mayer

Abstract

We consider a semiparametric competing risk model given by k independent survival times. The paper offers an asymptotic treatment of tests for the semiparametric null hypothesis of equality of the underlying risks. It turns out that modified rank tests are asymptotically efficient for certain semiparametric submodels, where the baseline hazard is a nuisance parameter. In addition, the asymptotic relative efficiency of the present tests is derived. A comparison of asymptotic power functions can then be used to classify various tests proposed earlier in the literature. For instance a chi-square type test is efficient for proportional hazards. Data driven tests of likelihood ratio type are proposed for cones of alternatives. We will consider certain stochastically increasing alternatives as a special example. The paper shows how the concept of local asymptotic normality of Le Cam works for hazard oriented models. Copyright Springer-Verlag 2004

Suggested Citation

  • Jan Beyersmann & Arnold Janssen & Claus-Dieter Mayer, 2004. "Efficient rank tests for semiparametric competing risk models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 60(1), pages 73-91, July.
    • Handle: RePEc:spr:metrik:v:60:y:2004:i:1:p:73-91
    DOI: 10.1007/s001840300297
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    Cited by:

    1. Félix Belzunce & Eva-María Ortega & Franco Pellerey & José Ruiz, 2007. "On rankings and top choices in random utility models with dependent utilities," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 66(2), pages 197-212, September.

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