High-Dimensional Time Series Segmentation via Factor-Adjusted Vector Autoregressive Modeling
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Publication:6631703
DOI10.1080/01621459.2023.2240054MaRDI QIDQ6631703
Hyeyoung Maeng, Idris A. Eckley, Paul Fearnhead, Haeran Cho
Publication date: 1 November 2024
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Cites Work
- Title not available (Why is that?)
- A MOSUM procedure for the estimation of multiple random change points
- Two-stage data segmentation permitting multiscale change points, heavy tails and dependence
- Optimal covariance change point localization in high dimensions
- Regularized estimation in sparse high-dimensional time series models
- A multiple filter test for the detection of rate changes in renewal processes with varying variance
- Factor-Adjusted Regularized Model Selection
- Simultaneous multiple change-point and factor analysis for high-dimensional time series
- Dynamic factor models with infinite-dimensional factor spaces: one-sided representations
- Dynamic factor models with infinite-dimensional factor space: asymptotic analysis
- Autoregressive models for gene regulatory network inference: sparsity, stability and causality issues
- The generalized dynamic factor model consistency and rates
- On the network topology of variance decompositions: measuring the connectedness of financial firms
- Time-varying general dynamic factor models and the measurement of financial connectedness
- Quasi-maximum likelihood estimation of break point in high-dimensional factor models
- A direct estimation of high dimensional stationary vector autoregressions
- Matrix Analysis
- Adaptive bandwidth choice
- Fast and Scalable Algorithm for Detection of Structural Breaks in Big VAR Models
- Multiple Change Points Detection in Low Rank and Sparse High Dimensional Vector Autoregressive Models
- Determining the Number of Factors in the General Dynamic Factor Model
- Detection of Multiple Structural Breaks in Multivariate Time Series
- Detection of Changes in Multivariate Time Series With Application to EEG Data
- Large Covariance Estimation by Thresholding Principal Orthogonal Complements
- Joint Structural Break Detection and Parameter Estimation in High-Dimensional Nonstationary VAR Models
- Inference of Breakpoints in High-dimensional Time Series
- Detection of Multiple Structural Breaks in Large Covariance Matrices
- A network analysis of the volatility of high dimensional financial series
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