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 intensi...
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Published in | Lifetime data analysis Vol. 17; no. 3; pp. 409 - 432 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Boston
Springer US
01.07.2011
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | 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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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1380-7870 1572-9249 |
DOI: | 10.1007/s10985-011-9193-1 |