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Detecting Multiple Random Changepoints in Bayesian Piecewise Growth Mixture Models.

Eric F Lock1, Nidhi Kohli2, Maitreyee Bose3

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, MMC 303 420 Delaware Street, S.E., Minneapolis, MN , 55455, USA. elock@umn.edu.

Psychometrika
|November 19, 2017
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Summary
This summary is machine-generated.

This study introduces a Bayesian approach for Piecewise Growth Mixture Models (PGMMs) to analyze segmented growth trajectories with multiple random changepoints within latent classes. The developed R package, BayesianPGMM, facilitates practical application and parameter estimation.

Keywords:
BayesianMarkov chain Monte Carlolongitudinal datamixture modelnonlinear random effects modelspiecewise function

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Area of Science:

  • Statistics
  • Developmental Psychology
  • Computational Biology

Background:

  • Piecewise Growth Mixture Models (PGMMs) analyze segmented trends in individual growth trajectories across latent classes.
  • Inferring changepoint locations, representing transitions between developmental phases, is crucial.
  • Existing methods may lack flexibility in handling multiple random changepoints within classes.

Purpose of the Study:

  • Develop a Bayesian PGMM for estimating multiple random changepoints within each latent class.
  • Create a procedure to empirically detect the number of random changepoints per class.
  • Investigate the bias and precision of parameter estimation, including random changepoints, through simulations.

Main Methods:

  • Bayesian inference for PGMMs with multiple random changepoints.
  • Development of a statistical procedure for changepoint number detection.
  • Simulation studies to assess parameter estimation accuracy and precision.
  • Implementation in the user-friendly R package BayesianPGMM.

Main Results:

  • The Bayesian PGMM framework successfully estimates multiple random changepoints within latent classes.
  • The developed procedure effectively detects the number of changepoints.
  • Simulation results demonstrate the bias and precision of parameter estimations.
  • The BayesianPGMM R package is available for practical use.

Conclusions:

  • The proposed Bayesian PGMM approach offers a flexible method for analyzing complex growth trajectories with individual differences in transition times.
  • The BayesianPGMM package enhances the accessibility of advanced statistical modeling for researchers.
  • This methodology is applicable to various fields, including behavioral science and developmental studies.