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Optimal goodness-of-fit tests for recurrent event data
Russell S Stocker1, Akim Adekpedjou
1Department of Mathematics, Indiana University of Pennsylvania, Indiana, PA 15705, USA. rstocker@iup.edu
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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