Abstract
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.
Original language | English |
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Title of host publication | 3rd International Conference on Statistics |
Subtitle of host publication | Theory and Applications, ICSTA 2021 |
Editors | Gangaram S. Ladde, Noelle Samia |
Publisher | Avestia Publishing |
ISBN (Print) | 9781927877913 |
DOIs | |
State | Published - 2021 |
Event | 3rd International Conference on Statistics: Theory and Applications, ICSTA 2021 - Virtual, Online Duration: Jul 29 2021 → Jul 31 2021 |
Publication series
Name | Proceedings of the International Conference on Statistics |
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ISSN (Electronic) | 2562-7767 |
Conference
Conference | 3rd International Conference on Statistics: Theory and Applications, ICSTA 2021 |
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City | Virtual, Online |
Period | 7/29/21 → 7/31/21 |
Bibliographical note
Publisher Copyright:© 2021, Avestia Publishing. All rights reserved.
Keywords
- 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