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Activity Number:
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445
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2008 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #302223 |
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Title:
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Model Assessment and Selection for Latent Transition Model
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Author(s):
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Hsiu-Ching Chang*+ and Hwan Chung+
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Companies:
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Michigan State University and Michigan State University
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Address:
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410 W Saginaw Street , East Lansing, MI, 48823, B601 West Fee Hall, East Lansing, MI, 48824,
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Keywords:
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Model Selection ; Simulation ; Latent Transition Model ; Longitudinal ; Stage-sequential
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Abstract:
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The Latent Transition Model (LTM) has recently been applied to behavioral and biomedical research to identify individuals' underlying stage-sequential processes. LTM derives from the family of latent class analysis in which manifest items are treated as fallible indicators of unseen states. Specifically, LTM is specified to estimate simultaneously the probability of individuals' membership in latent classes and their transition rate among these classes from occasion to occasion. As a particular example of finite mixtures, model selection is a difficult challenge facing users of LTM: different available methods often suggest different solutions, yielding ambiguity in model selection. Therefore, it is important to identify which method optimally recovers the true model under a variety conditions. In this talk, we review current available methods and compare their performance.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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