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A family of models for uniform and serial dependence in repeated measurements studies

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Abstract:

Data arising from a randomized double-masked clinical trial for multiple sclerosis have provided particularly variable longitudinal repeated measurements responses. Specific models for such data, other than those based on the multivariate normal distribution, would be a valuable addition to the applied statistician's toolbox. A useful family of multivariate distributions can be generated by substituting the integrated intensity of one distribution into a second (outer) distribution. The parameters in the second distribution are then used to create a dependence structure among observations on a unit. These may either be a form of serial dependence for longitudinal data or of uniform dependence within clusters. These are respectively analogous to the Kalman filter of state space models and to copulas, but they have the major advantage that they do not require any explicit integration. One useful outer distribution for constructing such multivariate distributions is the Pareto distribution. Certain special models based on it have previously been used in event history analysis, but those considered here have much wider application.

Keywords: Copula; Gamma distribution; Kalman filter; Laplace transform; Longitudinal study; Mixture distribution; Pareto distribution;; Random effect; Serial dependence

Document Type: Original Article

DOI: http://dx.doi.org/10.1111/1467-9876.00196

Affiliations: Limburgs Universitair Centrum, Diepenbeek, Belgium

Publication date: January 1, 2000

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