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Conditional adaptive Bayesian spectral analysis of replicated multivariate time series.

Zeda Li1, Scott A Bruce2, Clinton J Wutzke3

  • 1Paul H. Chook Department of Information Systems and Statistics, Baruch College, The City University of New York, New York, New York, USA.

Statistics in Medicine
|January 21, 2021
PubMed
Summary
This summary is machine-generated.

This study presents a new Bayesian method (MultiCABS) for analyzing time series power spectra, adapting to subject data and covariates. It accurately reveals associations, like fear of falling with Parkinson's disease postural control.

Keywords:
Bayesian analysisMarkov chain Monte Carlomultivariate time seriespower spectrum analysisreplicated time series

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Area of Science:

  • Statistics
  • Biostatistics
  • Time Series Analysis

Background:

  • Analyzing associations between covariates and power spectra of multivariate time series is complex.
  • Existing methods may not adequately capture group-specific dynamics or adapt to unknown data structures.

Purpose of the Study:

  • Introduce a flexible nonparametric Bayesian approach, multivariate conditional adaptive Bayesian power spectrum analysis (MultiCABS).
  • To accurately estimate and infer power spectra of multivariate time series across subjects with varying covariate values.

Main Methods:

  • Adaptive grouping of time series based on similar covariate values.
  • Nonparametric estimation of group-specific power spectra using penalized splines.
  • Fully Bayesian framework with random number of groups and covariate partitions, fitted via Markov chain Monte Carlo.

Main Results:

  • MultiCABS provides accurate estimation and inference for power spectra, handling both smooth and abrupt dynamics.
  • Simulation studies demonstrate superior performance compared to existing methods.
  • The method effectively analyzes associations between covariates and power spectra in real-world data.

Conclusions:

  • MultiCABS offers a robust and flexible approach for analyzing covariate-dependent power spectra in multivariate time series.
  • The methodology is particularly useful for complex biological and medical data, such as postural control in Parkinson's disease.
  • This Bayesian framework allows for data-driven discovery of underlying group structures and their spectral characteristics.