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Estimation and Inference in Nonparametric Cox-models: Time Transformation Methods

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Summary

In this paper generalization of the Cox proportional hazards regression model to a completely nonparametric model with an unspecified smooth covariate function is studied. A class of methods for Cox-regression called time transformation methods are defined, and a new method for nonparametric Cox-regression in this class is in particular studied. It turns out that this method enjoys a number of useful properties.

Ways of doing inference and model checking in nonparametric Cox-models are also discussed, and a brief overview and comparison of methods for nonparametric Cox-regression is given.

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Author information

Authors and Affiliations

  1. Department of Mathematical Sciences, Norwegian University of Science and Technology, N-7491, Trondheim, Norway

    Jan Terje Kvaløy & Bo Henry Lindqvist

  2. Department of Mathematics and Natural Science, Stavanger University College, P.O.Box 8002, N-4068, Stavanger, Norway

    Jan Terje Kvaløy

Authors
  1. Jan Terje Kvaløy
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  2. Bo Henry Lindqvist
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Additional information

Jan Terje Kvaløy was funded by a PhD grant from the Research Council of Norway during most of the work on this paper. We would like to thank the editors and referees for comments and suggestions that improved the paper.

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Kvaløy, J.T., Lindqvist, B.H. Estimation and Inference in Nonparametric Cox-models: Time Transformation Methods. Computational Statistics 18, 205–221 (2003). https://doi.org/10.1007/s001800300141

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