Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/10278
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | SERROYEN, Jan | - |
dc.contributor.author | MOLENBERGHS, Geert | - |
dc.contributor.author | VERBEKE, Geert | - |
dc.contributor.author | Davidian, Marie | - |
dc.date.accessioned | 2010-01-07T10:05:28Z | - |
dc.date.available | 2010-01-07T10:05:28Z | - |
dc.date.issued | 2009 | - |
dc.identifier.citation | AMERICAN STATISTICIAN, 63(4). p. 378-388 | - |
dc.identifier.issn | 0003-1305 | - |
dc.identifier.uri | http://hdl.handle.net/1942/10278 | - |
dc.description.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. | - |
dc.format.extent | 285295 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | AMER STATISTICAL ASSOC | - |
dc.rights | © 2009 American Statistical Association | - |
dc.subject.other | Conditional model; Marginal model; Random-effects model; Serial correlation; Transition model | - |
dc.subject.other | conditional model; marginal model; random-effects model; serial correlation; transition model | - |
dc.title | Nonlinear Models for Longitudinal Data | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 388 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 378 | - |
dc.identifier.volume | 63 | - |
local.format.pages | 11 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.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. | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.bibliographicCitation.oldjcat | A1 | - |
dc.identifier.doi | 10.1198/tast.2009.07256 | - |
dc.identifier.isi | 000271795500012 | - |
item.validation | ecoom 2010 | - |
item.contributor | SERROYEN, Jan | - |
item.contributor | MOLENBERGHS, Geert | - |
item.contributor | VERBEKE, Geert | - |
item.contributor | Davidian, Marie | - |
item.accessRights | Open Access | - |
item.fullcitation | SERROYEN, Jan; MOLENBERGHS, Geert; VERBEKE, Geert & Davidian, Marie (2009) Nonlinear Models for Longitudinal Data. In: AMERICAN STATISTICIAN, 63(4). p. 378-388. | - |
item.fulltext | With Fulltext | - |
crisitem.journal.issn | 0003-1305 | - |
crisitem.journal.eissn | 1537-2731 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
nonlin_taxo11.pdf | Peer-reviewed author version | 278.61 kB | Adobe PDF | View/Open |
serroyen2009.pdf Restricted Access | Published version | 429.55 kB | Adobe PDF | View/Open Request a copy |
SCOPUSTM
Citations
17
checked on Sep 2, 2020
WEB OF SCIENCETM
Citations
21
checked on Apr 30, 2024
Page view(s)
60
checked on Sep 7, 2022
Download(s)
118
checked on Sep 7, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.