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Activity Number:
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206
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Type:
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Contributed
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Date/Time:
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Monday, August 7, 2006 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #306255 |
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Title:
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Stochastic Models for MRI Lesion Count Data from Patients with Relapsing Remitting Multiple Sclerosis
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Author(s):
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Xiaobai Li*+ and H. N. Nagaraja
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Companies:
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The Ohio State University and The Ohio State University
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Address:
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4490 Westborough Drive, W., Columbus, OH, 43220,
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Keywords:
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RRMS ; MRI ; queueing theory ; hidden Markov
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Abstract:
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The longitudinal T1-weighted Gadolinium-enhancing MRI lesion count sequences provide information on the onset and sojourn time of the lesion enhancement for patients with relapsing remitting multiple sclerosis (RRMS). The infinite-server queue with Poisson arrival process and exponential service is proposed for this type of data. The rate of the Poisson arrival process can also be allowed to be governed by a two-state hidden Markov chain. We describe the likelihood function for each model based on appropriate assumptions and fit these models to a dataset from 9 RRMS patients. We obtain the maximum likelihood estimators of the parameters of interest arising from these models and study their asymptotic properties through simulation. We discuss the validation of the assumptions for the proposed models and examine the robustness of these estimators. We suggest the application of the models.
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