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Optimal goodness-of-fit tests for recurrent event data

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Abstract

A class of tests for the hypothesis that the baseline intensity belongs to a parametric class of intensities is given in the recurrent event setting. Asymptotic properties of a weighted general class of processes that compare the non-parametric versus parametric estimators for the cumulative intensity are presented. These results are given for a sequence of Pitman alternatives. Test statistics are proposed and methods of obtaining critical values are examined. Optimal choices for the weight function are given for a class of chi-squared tests. Based on Khmaladze’s transformation we propose distributional free tests. These include the types of Kolmogorov–Smirnov and Cramér–von Mises. The tests are used to analyze two different data sets.

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Authors and Affiliations

  1. Department of Mathematics, Indiana University of Pennsylvania, 210 South Tenth Street, Indiana, PA, 15705, USA

    Russell S. Stocker IV

  2. Department of Mathematics and Statistics, Missouri University of Science and Technology, 300 W 13th Street, Rolla, MO, 65409, USA

    Akim Adekpedjou

Authors
  1. Russell S. Stocker IV
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  2. Akim Adekpedjou
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Correspondence to Russell S. Stocker IV.

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Stocker, R.S., Adekpedjou, A. Optimal goodness-of-fit tests for recurrent event data. Lifetime Data Anal 17, 409–432 (2011). https://doi.org/10.1007/s10985-011-9193-1

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  • DOI: https://doi.org/10.1007/s10985-011-9193-1

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