Logistic Growth Modeling with Markov Chain Monte Carlo Estimation

Logistic Growth Modeling with Markov Chain Monte Carlo Estimation

A 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 e...

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Veröffentlicht in:Journal of modern applied statistical methods 2020-04, Vol.18 (1), p.2-18
Hauptverfasser: Choi, Jaehwa, Chen, Jinsong, Harring, Jeffery R.
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creator Choi, Jaehwa
Chen, Jinsong
Harring, Jeffery R.
description A 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.
doi_str_mv 10.22237/jmasm/1556669820
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