Stochastic Version of EM Algorithm for Nonlinear Random Change-Point Models

Hongbin Zhang, Binod Manandhar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publication3rd International Conference on Statistics
Subtitle of host publicationTheory and Applications, ICSTA 2021
EditorsGangaram S. Ladde, Noelle Samia
PublisherAvestia Publishing
ISBN (Print)9781927877913
DOIs
StatePublished - 2021
Event3rd International Conference on Statistics: Theory and Applications, ICSTA 2021 - Virtual, Online
Duration: Jul 29 2021Jul 31 2021

Publication series

NameProceedings of the International Conference on Statistics
ISSN (Electronic)2562-7767

Conference

Conference3rd International Conference on Statistics: Theory and Applications, ICSTA 2021
CityVirtual, Online
Period7/29/217/31/21

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

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