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Activity Number: 74
Type: Contributed
Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract - #306714
Title: A Generalized Chi-Square Goodness-of-Fit Test for Recurrent Failure Time Data
Author(s): Withanage De Mel and Akim Adekpedjou*+ and Gideon K.D. Zamba
Companies: Missouri University of Science and Technology and Missouri University of Science and Technology and University of Iowa
Address: Mathematics and Statistics, Rolla, MO, 65401, United States
Keywords: Recurrent Failure ; Goodness of Fit ; Minimum Distance Estimator ; Gaussian Process ; Composie hypotheses

The problem of goodness of fit of the distribution function F governing the time to occurrence of a recurrent event is considered. Specifically, we develop a test for the null hypothesis that F belongs to some parametric family \mathcal{F}(t, \theta}. We derive a generalization of the chi-square goodness of fit (Pearson-like) test based on a nonparametric maximum likelihood estimator of F which possesses asymptotic properties such as consistency and convergence to Gaussian processes. The parameter \theta is estimated by the value that minimizes the distance between the vector of observed and expected frequency over k non-overlapping partitions. We prove that the test statistic is asymptotically chi-square distributed. We provide results of a simulation study and application to real recurrent failure time data.

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