Optimal Goodness-of-Fit Tests for Recurrent Event Data
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.
R. S. Stocker and A. Adekpedjou, "Optimal Goodness-of-Fit Tests for Recurrent Event Data," Lifetime Data Analysis, Springer Verlag, Jul 2011.
The definitive version is available at https://doi.org/10.1007/s10985-011-9193-1
Mathematics and Statistics
Keywords and Phrases
Counting Processes; Effective Age; Khmaladze's Transformation; Martingales; Stochastic Integration; Goodness-Of-Fit Tests
Article - Journal
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