Statistical Inference for High-Dimensional Vector Autoregression with Measurement Error

DOI10.48550/arXiv.2009.08011arXiv2009.08011OpenAlexW3085311148WikidataQ114013792 ScholiaQ114013792MaRDI QIDQ134115

Jian Kang, Lexin Li, Xiang Lyu, Jian Kang, Lexin Li, Unnamed Author

Publication date: 17 September 2020

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2009.08011

zbMATH Keywords

expectation-maximizationvector autoregressionglobal testingsimultaneous testingbrain network analysiscovariance inference

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

hdiVAR

Cites Work

• Unnamed Item
• Gaussian graphical model estimation with false discovery rate control
• Phase transition and regularized bootstrap in large-scale \(t\)-tests with false discovery rate control
• Statistical guarantees for the EM algorithm: from population to sample-based analysis
• A general theory of hypothesis tests and confidence regions for sparse high dimensional models
• Estimation of (near) low-rank matrices with noise and high-dimensional scaling
• Limiting laws of coherence of random matrices with applications to testing covariance structure and construction of compressed sensing matrices
• Subset selection for vector autoregressive processes using Lasso
• Bootstrap based inference for sparse high-dimensional time series models
• Testing for high-dimensional network parameters in auto-regressive models
• CHIME: clustering of high-dimensional Gaussian mixtures with EM algorithm and its optimality
• Asymptotic theory for maximum deviations of sample covariance matrix estimates
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Multiple Testing of Submatrices of a Precision Matrix With Applications to Identification of Between Pathway Interactions
• Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings
• Tests for High-Dimensional Covariance Matrices
• A Dynamic Directional Model for Effective Brain Connectivity Using Electrocorticographic (ECoG) Time Series
• Measurement Error in Linear Autoregressive Models