Abstract
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
10.22237/jmasm/1556669820
Recommended Citation
Choi, J., Chen, J., & Harring, J. R. (2019). Logistic growth modeling with Markov chain Monte Carlo estimation. Journal of Modern Applied Statistical Methods, 18(1), eP2997. doi: 10.22237/jmasm/1556669820
Included in
Applied Statistics Commons, Social and Behavioral Sciences Commons, Statistical Theory Commons
