Journal article
Modeling Nonstationary Longitudinal Data
Biometrics, Vol.56(3), pp.699-705
Received January 1999. Revised February 2000. Accepted February 2000.
09/2000
DOI: 10.1111/j.0006-341X.2000.00699.x
PMID: 10985205
Abstract
An important theme of longitudinal data analysis in the past two decades has been the development and use of explicit parametric models for the data's variancecovariance structure. A variety of these models have been proposed, of which most are second-order stationary. A few are flexible enough to accommodate nonstationarity, i.e., nonconstant variances and/or correlations that are not a function solely of elapsed time between measurements. We review five nonstationary models that we regard as most useful: (1) the unstructured covariance model, (2) unstructured antedependence models, (3) structured antedependence models, (4) autoregressive integrated moving average and similar models, and (5) random coefficients models. We evaluate the relative strengths and limitations of each model, emphasizing when it is inappropriate or unlikely to be useful. We present three examples to illustrate the fitting and comparison of the models and to demonstrate that nonstationary longitudinal data can be modeled effectively and, in some cases, quite parsimoniously. In these examples, the antedependence models generally prove to be superior and the random coefficients models prove to be inferior. We conclude that antedependence models should be given much greater consideration than they have historically received.
Details
- Title: Subtitle
- Modeling Nonstationary Longitudinal Data
- Creators
- Vicente Núñez-Antón - University of the Basque CountryDale L Zimmerman - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Biometrics, Vol.56(3), pp.699-705
- Edition
- Received January 1999. Revised February 2000. Accepted February 2000.
- Publisher
- Blackwell Publishing Ltd
- DOI
- 10.1111/j.0006-341X.2000.00699.x
- PMID
- 10985205
- ISSN
- 0006-341X
- eISSN
- 1541-0420
- Number of pages
- 7
- Language
- English
- Date published
- 09/2000
- Academic Unit
- Statistics and Actuarial Science; Biostatistics
- Record Identifier
- 9984257739802771
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