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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/10278

Title: Nonlinear Models for Longitudinal Data
Authors: Serroyen, Jan
Molenberghs, Geert
Verbeke, Geert
Davidian, Marie
Issue Date: 2009
Citation: AMERICAN STATISTICIAN, 63(4). p. 378-388
Abstract: Whereas marginal models, random-effects models, and conditional models are routinely considered to be the three main modeling families for continuous and discrete repeated measures with linear and generalized linear mean structures, respectively, it is less common to consider nonlinear models, let alone frame them within the above taxonomy. In the latter situation, indeed, when considered at all, the focus is often exclusively on random-effects models. In this article, we consider all three families, exemplify their great flexibility and relative ease of use, and apply them to a simple but illustrative set of data on tree circumference growth of orange trees. This article has supplementary material online.
Notes: [Serroyen, Jan] Univ Maastricht, Dept Methodol & Stat, NL-6229 HA Maastricht, Netherlands. [Molenberghs, Geert; Verbeke, Geert] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium. [Molenberghs, Geert; Verbeke, Geert] Katholieke Univ Leuven, B-3000 Louvain, Belgium. [Davidian, Marie] N Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA.
URI: http://hdl.handle.net/1942/10278
DOI: 10.1198/tast.2009.07256
ISI #: 000271795500012
ISSN: 0003-1305
Category: A1
Type: Journal Contribution
Validation: ecoom, 2010
Appears in Collections: Research publications

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