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Abstract Details
Activity Number:
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169
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract - #300944 |
Title:
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A Chi-Squared Type Goodness of Fit Test for Recurrent Event Data
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Author(s):
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Akim Adekpedjou*+ and Gideon Zamba+
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Companies:
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Missouri University of Science and Technology and University of Iowa
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Address:
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, Rolla, MO, 65409, Department of Biostatistics, Iowa City, ,
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Keywords:
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Recurrent Events ;
Gaussian Process ;
Pitman's Alternative ;
Goodness of Fit
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Abstract:
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Goodness of fit of the distribution function governing the time to occurrence of a recurrent event is considered. We develop a chi-squared type of test based on a nonparametric maximum likelihood estimator (NPMLE) for testing the distribution function of the inter event time of recurrent event data. The test is of the inter-event time distribution for recurrent events. The test compares a parametric null to the NPMLE over $k$ partitions of a calendar time $s$. We investigate small sample and asymptotic properties of the test as well as power analysis against a sequence of Pitman's alternatives. Four variants of the test resulting from a combination of variance estimators and censoring were studied. The conclusion that transpires from the finite sample simulation study is that significant level is achieved when the right-censoring random variable is not ignored and $k > 3$. For exponential model, the tests are less powered to detect lighter right tail distribution than they are for left tails (contrary to Weibull model findings). The Weibull model has a slow response to heavier left tail distributions. We apply the test to a real-life recurrent event data.
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Authors who are presenting talks have a * after their name.
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