activity
20182025
most citedBrain Waves Analysis Via a Non-parametric Bayesian Mixture of Autoregressive Kernels

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

stat.ME2025

A Bayesian Integrative Mixed Modeling Framework for Analysis of the Adolescent Brain and Cognitive Development Study

Aidan Neher, Apostolos Stamenos, Mark Fiecas +2

Integrating high-dimensional, heterogeneous data from multi-site cohort studies with complex hierarchical structures poses significant feature selection and prediction challenges.…

stat.ME20211 cited

Brain Waves Analysis Via a Non-parametric Bayesian Mixture of Autoregressive Kernels

Guillermo Granados-Garcia, Mark Fiecas, Babak Shahbaba +2

The standard approach to analyzing brain electrical activity is to examine the spectral density function (SDF) and identify predefined frequency bands that have the most substantia…

stat.ME2020

Identifying the Recurrence of Sleep Apnea Using a Harmonic Hidden Markov Model

Beniamino Hadj-Amar, Bärbel Finkenstädt, Mark Fiecas +1

We propose to model time-varying periodic and oscillatory processes by means of a hidden Markov model where the states are defined through the spectral properties of a periodic reg…

math.ST2018

Spectral Analysis of High-dimensional Time Series

Mark Fiecas, Chenlei Leng, Weidong Liu +1

A useful approach for analysing multiple time series is via characterising their spectral density matrix as the frequency domain analog of the covariance matrix. When the dimension…

stat.ME2018

Bayesian Model Search for Nonstationary Periodic Time Series

Beniamino Hadj-Amar, Bärbel Finkenstädt, Mark Fiecas +2

We propose a novel Bayesian methodology for analyzing nonstationary time series that exhibit oscillatory behaviour. We approximate the time series using a piecewise oscillatory mod…