Random effect change-point models are commonly used to infer individual-specific time of event that induces trend change of longitudinal data. Linear models are often employed before and after the change point. However, in applications such as HIV studies, a mechanistic nonlinear model can be derived for the process based on the underlying data-generation mechanisms and such nonlinear model may provide better ``predictions". In this article, we propose a random change-point model in which we model the longitudinal data by segmented nonlinear mixed effect models. Inference wise, we propose a maximum likelihood solution where we use the Stochastic Expectation-Maximization (StEM) algorithm coupled with independent multivariate rejection sampling through Gibbs’s sampler. We evaluate the method with simulations to gain insights.
|Title of host publication||3rd International Conference on Statistics|
|Subtitle of host publication||Theory and Applications, ICSTA 2021|
|Editors||Gangaram S. Ladde, Noelle Samia|
|State||Published - 2021|
|Event||3rd International Conference on Statistics: Theory and Applications, ICSTA 2021 - Virtual, Online|
Duration: Jul 29 2021 → Jul 31 2021
|Name||Proceedings of the International Conference on Statistics|
|Conference||3rd International Conference on Statistics: Theory and Applications, ICSTA 2021|
|Period||7/29/21 → 7/31/21|
Bibliographical noteFunding Information:
The work is supported by NIH grant R21AI147933. It is also partially supported by the City University of New York High-Performance Computing Center, College of Staten Island, funded in part by the City and State of New York, City University of New York Research Foundation and National Science Foundation grants CNS-0958379, CNS-0855217, and ACI-112611.
© 2021, Avestia Publishing. All rights reserved.
- Gibbs’s sampler
- Multivariate rejection sampling
- Nonlinear mixed effects model
- Random change-point model
- Stochastic version of EM
ASJC Scopus subject areas
- Applied Mathematics
- Computational Mathematics
- Statistics and Probability
- Theoretical Computer Science