Logistic Growth Modeling with Markov Chain Monte Carlo Estimation-Reference-Cited by-同舟云学术

Logistic Growth Modeling with Markov Chain Monte Carlo Estimation

Published:2020-04-28 Issue:1 Volume:18 Page:2-18
ISSN:1538-9472
Container-title:Journal of Modern Applied Statistical Methods
language:en
Short-container-title:J. Mod. Appl. Stat. Methods

Author:

Choi Jaehwa¹,Chen Jinsong²,Harring Jeffery R.³

Affiliation:

1. The George Washington University

2. Sun Yen-Sat University, Guangzhou, China

3. University of Maryland

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.

Publisher

Wayne State University Library System

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference25 articles.

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2. Conditionally Linear Mixed-Effects Models With Latent Variable Covariates

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4. Latent Curve Models

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