Reconstruction of missing data in multivariate processes with applications to causality analysis

DOI10.1007/s12572-017-0198-1OpenAlexW2771434267MaRDI QIDQ1637498

Piyush Agarwal, Arun K. Tangirala

Publication date: 8 June 2018

Published in: International Journal of Advances in Engineering Sciences and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s12572-017-0198-1

zbMATH Keywords

random samplingmissing datasparse optimizationcausal graph

Mathematics Subject Classification ID

Uses Software

• iVAR

Cites Work

• Unnamed Item
• The restricted isometry property and its implications for compressed sensing
• Partial directed coherence: a new concept in neural structure determination
• Fusion of sparse reconstruction algorithms for multiple measurement vectors
• On the evaluation of information flow in multivariate systems by the directed transfer function
• Quantitative analysis of directional strengths in jointly stationary linear multivariate processes
• Identification of ARX-models subject to missing data
• Sparse and Redundant Representations
• Investigating Causal Relations by Econometric Models and Cross-spectral Methods
• Compressed sensing

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