IngentaConnect Modeling Nonstationary Longitudinal Data

Authors: Núñez-Antón, Vicente¹; Zimmerman, Dale L.²

Source: Biometrics, Volume 56, Number 3, September 2000 , pp. 699-705(7)

Abstract:

Summary.

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 variance-covariance 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.

Keywords: Antedependence; ARIMA models; Covariance structure; Heteroscedasticity; Mixed models; Nonstationary correlations; Random coefficients; Unstructured covariance

Document Type: Research article

DOI: 10.1111/j.0006-341X.2000.00699.x

Affiliations: 1: Departamento de Econometría y Estadística, Universidad del País Vasco, Avenida del Lehendakari Aguirre 83, 48015 Bilbac-Vizcaya, Spain 2: Department of Statistics and Actuarial Science, The University of Iowa, Iowa City, Iowa 52242, USA., Email: dzimmer@stat.uiowa.edu

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