Beyond Linear Dynamic Functional Connectivity: A Vine Copula Change Point Model

DOI10.1080/10618600.2022.2127738OpenAlexW4297019279MaRDI QIDQ6140311

Xin Xiong, Ivor Cribben

Publication date: 22 January 2024

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2022.2127738

zbMATH Keywords

graphical modelscomputational statisticstime-varying networksnetworks: fMRI

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• Selecting and estimating regular vine copulae and application to financial returns
• A MOSUM procedure for the estimation of multiple random change points
• Wild binary segmentation for multiple change-point detection
• High-dimensional classification using features annealed independence rules
• Break detection in the covariance structure of multivariate time series models
• Estimating the dimension of a model
• Analyzing dependent data with vine copulas. A practical guide with R
• Parameter estimation for pair-copula constructions
• Testing for instability in covariance structures
• Modeling dependence via copula of functionals of Fourier coefficients
• Uncertainty Analysis with High Dimensional Dependence Modelling
• Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance
• The Stationary Bootstrap
• Testing and Locating Variance Changepoints with Application to Stock Prices
• Nonparametric change point detection in multivariate piecewise stationary time series
• A semiparametric estimation procedure of dependence parameters in multivariate families of distributions
• A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data
• Nonparametric Anomaly Detection on Time Series of Graphs
• A Likelihood Ratio Approach to Sequential Change Point Detection for a General Class of Parameters
• FreSpeD: Frequency-Specific Change-Point Detection in Epileptic Seizure Multi-Channel EEG Data
• Detection of Changes in Multivariate Time Series With Application to EEG Data
• Multiple-Change-Point Detection for High Dimensional Time Series via Sparsified Binary Segmentation
• Detecting Relevant Changes in Time Series Models

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