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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/9041

Title: Multivariate distributions with correlation matrices for nonlinear repeated measurements
Authors: LINDSEY, James
LINDSEY, Patrick
Issue Date: 2006
Publisher: ELSEVIER SCIENCE BV
Citation: COMPUTATIONAL STATISTICS & DATA ANALYSIS, 50(3). p. 720-732
Abstract: The polynomial growth curve model based on the multivariate normal distribution has dominated the analysis of continuous longitudinal repeated measurements for the last 50 years. The main reasons include the ease of modelling dependence because of the availability of the correlation matrix and the linearity of the regression coefficients. However, a variety of other useful distributions also involve a correlation matrix: the multivariate Student's t, multivariate power exponential, and multivariate skew Laplace distributions as well as Gaussian copulas with arbitrarily chosen marginal distributions. With modern computing power and software, nonlinear regression functions can be fitted as easily as linear ones. By a number of examples, we show that these distributions, combined with nonlinear regression functions, generally yield an improved fit, as compared to the standard polynomial growth curve model, and can provide different conclusions. (c) 2004 Elsevier B.V. All rights reserved.
Notes: Limburgs Univ Ctr, Diepenbeek, Belgium. Eurandom, Eindhoven, Netherlands.
URI: http://hdl.handle.net/1942/9041
ISI #: 000232738400009
ISSN: 0167-9473
Category: A1
Type: Journal Contribution
Validation: ecoom, 2006
Appears in Collections: Research publications

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