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Abstract Details
Activity Number:
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27
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #302561 |
Title:
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Classification of Multivariate Longitudinal Data Through a Semiparametric Mixed-Effects Model with Nonstandard Assumptions
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Author(s):
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Rolando De la Cruz and Cristian Meza*+
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Companies:
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Pontificia Universidad Católica de Chile and Universidad de Valparaíso
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Address:
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Department of Statistics, , ,
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Keywords:
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Classification ;
Mixed-effects ;
Multiple responses ;
Splines ;
Skew-normal distribution ;
EM algorithm
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Abstract:
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Multivariate longitudinal data arise when a set of different responses on the same subject are measured repeatedly over time. This talk discusses model-based statistical methods for the classification of subjects into one of two or more groups based on multivariate longitudinal data. We relate the observed responses using a semiparametric multivariate linear mixed-effects models (SMLMM) to describe evolutions in different groups. The SMLMM is formulated with fixed-effects, random effects and with a smooth function to model the time effect. For each of the response variables, the smooth function is estimated via low-rank cubic splines using radial basis functions. In the SMLMM the error term and the random effects are assumed to follow a multivariate skew-normal distribution. Parameter estimation is done via the expectation-maximization algorithm. Also, we consider the case of missing responses. We present an example using data from a study in 161 pregnant women in Santiago, Chile, where the main interest is to predict normal versus abnormal pregnancy outcomes.
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Authors who are presenting talks have a * after their name.
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