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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Bayesian adaptive group lasso with semiparametric hidden Markov models.

Kai Kang1, Xinyuan Song1,2, X Joan Hu3

  • 1Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong.

Statistics in Medicine
|November 29, 2018
PubMed
Summary

This study introduces a Bayesian method for analyzing complex diseases like Alzheimer's, identifying key risk factors for cognitive decline and disease progression using hidden Markov models.

Keywords:
Markov chain Monte Carlolinear basis expansionsimultaneous model selection and estimation

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Area of Science:

  • Statistics
  • Biostatistics
  • Machine Learning

Background:

  • Semiparametric hidden Markov models (HMMs) are complex statistical tools.
  • Simultaneous model selection and estimation are crucial for accurate analysis.
  • Identifying covariate effects in HMMs requires advanced methodologies.

Purpose of the Study:

  • To develop a Bayesian adaptive group least absolute shrinkage and selection operator (LASSO) method.
  • To enable simultaneous model selection and estimation in semiparametric HMMs.
  • To identify various forms of covariate effects (nonexistent, constant, linear, nonlinear) in both conditional and transition models.

Main Methods:

  • Utilizing additive nonparametric functions for conditional regression and transition probability models.
  • Employing basis expansion for nonparametric function approximation.
  • Introducing multivariate conditional Laplace priors for adaptive penalties on coefficients and basis expansions.
  • Developing an efficient Markov chain Monte Carlo (MCMC) algorithm for estimation and selection.

Main Results:

  • The proposed Bayesian adaptive group LASSO method effectively performs simultaneous model selection and estimation.
  • The MCMC algorithm successfully identifies different forms of covariate effects.
  • Simulation studies demonstrate the empirical performance of the methodology.
  • Application to Alzheimer's Disease Neuroimaging Initiative data reveals significant risk factors for cognitive decline.

Conclusions:

  • The developed Bayesian approach offers a robust framework for semiparametric HMM analysis.
  • The method successfully identifies crucial risk factors influencing cognitive decline and disease progression in Alzheimer's disease.
  • This methodology enhances understanding of complex disease dynamics and aids in risk factor identification.