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Article: Logistic Growth Modeling with Markov Chain Monte Carlo Estimation

TitleLogistic Growth Modeling with Markov Chain Monte Carlo Estimation
Authors
Choi, JChen, JHarring, JR
KeywordsGrowth modeling
Latent growth modeling
Nonlinear growth models
Logistic functions
Markov chain Monte Carlo
Bayesian inference
Issue Date2019
PublisherWayne State University, College of Education. The Journal's web site is located at http://www.jmasm.com/
Citation
Journal of Modern Applied Statistical Methods, 2019, v. 18 n. 1, article no. eP2997 How to Cite?
DOI: http://dx.doi.org/10.22237/jmasm/1556669820
AbstractA new growth modeling approach is proposed to can fit inherently nonlinear (i.e., logistic) function without constraint nor reparameterization. A simulation study is employed to investigate the feasibility and performance of a Markov chain Monte Carlo method within Bayesian estimation framework to estimate a fully random version of a logistic growth curve model under manipulated conditions such as the number and timing of measurement occasions and sample sizes.
Persistent Identifierhttp://hdl.handle.net/10722/289300
ISSN
1538-9472
2023 SCImago Journal Rankings: 0.174
ISI Accession Number ID
WOS:000605728200012

 

DC FieldValueLanguage
dc.contributor.authorChoi, J-
dc.contributor.authorChen, J-
dc.contributor.authorHarring, JR-
dc.date.accessioned2020-10-22T08:10:42Z-
dc.date.available2020-10-22T08:10:42Z-
dc.date.issued2019-
dc.identifier.citationJournal of Modern Applied Statistical Methods, 2019, v. 18 n. 1, article no. eP2997-
dc.identifier.issn1538-9472-
dc.identifier.urihttp://hdl.handle.net/10722/289300-
dc.description.abstractA new growth modeling approach is proposed to can fit inherently nonlinear (i.e., logistic) function without constraint nor reparameterization. A simulation study is employed to investigate the feasibility and performance of a Markov chain Monte Carlo method within Bayesian estimation framework to estimate a fully random version of a logistic growth curve model under manipulated conditions such as the number and timing of measurement occasions and sample sizes.-
dc.languageeng-
dc.publisherWayne State University, College of Education. The Journal's web site is located at http://www.jmasm.com/-
dc.relation.ispartofJournal of Modern Applied Statistical Methods-
dc.subjectGrowth modeling-
dc.subjectLatent growth modeling-
dc.subjectNonlinear growth models-
dc.subjectLogistic functions-
dc.subjectMarkov chain Monte Carlo-
dc.subjectBayesian inference-
dc.titleLogistic Growth Modeling with Markov Chain Monte Carlo Estimation-
dc.typeArticle-
dc.identifier.emailChen, J: jinsong@HKUCC-COM.hku.hk-
dc.identifier.authorityChen, J=rp02740-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.22237/jmasm/1556669820-
dc.identifier.scopuseid_2-s2.0-85097161543-
dc.identifier.hkuros316821-
dc.identifier.volume18-
dc.identifier.issue1-
dc.identifier.spagearticle no. eP2997-
dc.identifier.epagearticle no. eP2997-
dc.identifier.isiWOS:000605728200012-
dc.publisher.placeUnited States-
dc.identifier.issnl1538-9472-

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