The Impact of Prior Information on Bayesian Latent Basis Growth Model Estimation
Latent basis growth modeling is a flexible version of the growth curve modeling, in which it allows the basis coefficients of the model to be freely estimated, and thus the optimal growth trajectories can be determined from the observed data. In this article, Bayesian estimation methods are applied...
Main Authors: | Dingjing Shi, Xin Tong |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2017-08-01
|
Series: | SAGE Open |
Online Access: | https://doi.org/10.1177/2158244017727039 |
Similar Items
-
A Bayesian Approach to the Analysis of Local Average Treatment Effect for Missing and Non-normal Data in Causal Modeling: A Tutorial With the ALMOND Package in R
by: Dingjing Shi, et al.
Published: (2020-02-01) -
Corrigendum: A Bayesian Approach to the Analysis of Local Average Treatment Effect for Missing and Non-normal Data in Causal Modeling: A Tutorial With the ALMOND Package in R
by: Dingjing Shi, et al.
Published: (2020-09-01) -
Assessing the Impact of Precision Parameter Prior in Bayesian Non-parametric Growth Curve Modeling
by: Xin Tong, et al.
Published: (2021-03-01) -
Bayesian Testing of a Point Null Hypothesis Based on the Latent Information Prior
by: Fumiyasu Komaki
Published: (2013-10-01) -
The impact of prior information on estimates of disease transmissibility using Bayesian tools.
by: Carlee B Moser, et al.
Published: (2015-01-01)