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Basics of Multivariate Analysis in Neuroimaging Data
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Multivariate Discrete Hidden Markov Models for Domain-Based Measurements and Assessment of Risk Factors in Child

Qiang Zhang1, Alison Snow Jones2, Frank Rijmen3

  • 1Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC 27159.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|January 10, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces advanced hidden Markov models for analyzing complex child development data. These models effectively capture individual traits and measurement clusters across multiple data waves.

Keywords:
Junction treeMixed effectsNational Longitudinal Survey of Youth

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Area of Science:

  • Social and Behavioral Sciences
  • Developmental Psychology
  • Biostatistics

Background:

  • Multivariate discrete measurements are common in social and behavioral research.
  • These measurements often exhibit underlying individual traits, clustered domains, and longitudinal data.
  • Analyzing complex developmental data requires sophisticated statistical modeling.

Purpose of the Study:

  • To propose extended multivariate discrete hidden Markov models for analyzing domain-based measurements of child cognition and behavior.
  • To incorporate a random effects model to capture long-term individual traits.
  • To develop a model selection criterion using the Bayes factor for these extended models.

Main Methods:

  • Development of extended multivariate discrete hidden Markov models.
  • Integration of a random effects model to account for individual trait variability.
  • Application of a Bayes factor-based model selection criterion.

Main Results:

  • The proposed models effectively analyze domain-based measurements in child development.
  • The random effects component successfully captures underlying individual traits.
  • The Bayes factor provides a robust method for model selection in this context.

Conclusions:

  • The extended multivariate discrete hidden Markov models offer a powerful framework for analyzing complex longitudinal data in child development.
  • The methods are illustrated using the National Longitudinal Survey of Youth (NLSY).
  • Supplementary technical details and computer codes are available for reproducibility.