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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/2929

Title: A family of models for uniform and serial dependence in repeated measurements studies
Authors: LINDSEY, James
Issue Date: 2000
Publisher: BLACKWELL PUBL LTD
Citation: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 49. p. 343-357
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.
Notes: Limburgs Univ Ctr, Dept Biostat, B-3590 Diepenbeek, Belgium.Lindsey, JK, Limburgs Univ Ctr, Dept Biostat, Univ Campus, B-3590 Diepenbeek, Belgium.
URI: http://hdl.handle.net/1942/2929
DOI: 10.1111/1467-9876.00196
ISI #: 000088295400004
ISSN: 0035-9254
Category: A1
Type: Journal Contribution
Validation: ecoom, 2001
Appears in Collections: Research publications

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