Optimal Goodness-of-Fit Tests for Recurrent Event Data

Author

Russell S. Stocker
Akim Adekpedjou, Missouri University of Science and TechnologyFollow

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.

Recommended Citation

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

Department(s)

Mathematics and Statistics

Keywords and Phrases

Counting Processes; Effective Age; Khmaladze's Transformation; Martingales; Stochastic Integration; Goodness-Of-Fit Tests

International Standard Serial Number (ISSN)

1380-7870

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2011 Springer Verlag, All rights reserved.

Publication Date

01 Jul 2011