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Modeling covariance structure in unbalanced longitudinal data

Modeling covariance structure is important for efficient estimation in longitudinal
data models. Modified Cholesky decomposition (Pourahmadi, 1999) is used as an
unconstrained reparameterization of the covariance matrix. The resulting new parameters
have transparent statistical interpretations and are easily modeled using
covariates. However, this approach is not directly applicable when the longitudinal
data are unbalanced, because a Cholesky factorization for observed data that is
coherent across all subjects usually does not exist. We overcome this difficulty by
treating the problem as a missing data problem and employing a generalized EM
algorithm to compute the ML estimators. We study the covariance matrices in both
fixed-effects models and mixed-effects models for unbalanced longitudinal data. We
illustrate our method by reanalyzing Kenwards (1987) cattle data and conducting
simulation studies.

Identiferoai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-3073
Date15 May 2009
CreatorsChen, Min
ContributorsHuang, Jianhua
Source SetsTexas A and M University
Languageen_US
Detected LanguageEnglish
TypeBook, Thesis, Electronic Dissertation, text
Formatelectronic, application/pdf, born digital

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