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.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-3073 |
| Date | 15 May 2009 |
| Creators | Chen, Min |
| Contributors | Huang, Jianhua |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | electronic, application/pdf, born digital |
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