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Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

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A wavelet-based Bayesian approach to regression models with long memory errors and its application to FMRI data.

Jaesik Jeong¹, Marina Vannucci, Kyungduk Ko

¹Department of Biostatistics, Indiana University, Indianapolis, Indiana 46202, USA.

|February 6, 2013

Summary

This summary is machine-generated.

This study introduces a Bayesian method using wavelet transforms to accurately estimate parameters in linear regression models with long memory errors, improving analysis in fields like econometrics and medical imaging.

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Area of Science:

Statistics
Signal Processing
Econometrics

Background:

Linear regression models with long memory errors are valuable in diverse fields.
Wavelets exhibit self-similarity, aligning with long memory data characteristics.

Purpose of the Study:

To develop an accurate estimation method for linear regression models with long memory errors.
To simplify complex data structures using wavelet transforms.

Main Methods:

Employing discrete wavelet transforms as whitening filters to manage dense variance-covariance matrices.
Adopting a Bayesian approach for parameter estimation using exact wavelet coefficients variances.

Main Results:

Accurate estimation of model parameters was achieved.
Simulated data performance was explored.
Application to fMRI data demonstrated utility.

Conclusions:

The proposed Bayesian wavelet method provides accurate parameter estimates for long memory models.
Posterior probability maps (PPMs) derived from fMRI data aid in identifying activated voxels with confidence.