A Data-Driven McMillan Degree Lower Bound

DOI10.1137/18M1194481zbMath1456.37098arXiv1803.00043OpenAlexW3097338623MaRDI QIDQ5146675

Jeffrey Hokanson

Publication date: 26 January 2021

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1803.00043

zbMATH Keywords

Hankel matrixrandom matrixmodel order selectionMcMillan degree

Mathematics Subject Classification ID

Random matrices (probabilistic aspects) (60B20) Identification in stochastic control theory (93E12) Random matrices (algebraic aspects) (15B52) Statistical aspects of information-theoretic topics (62B10) Approximation methods and numerical treatment of dynamical systems (37M99)

Related Items (1)

Learning Low-Dimensional Dynamical-System Models from Noisy Frequency-Response Data with Loewner Rational Interpolation

Uses Software

• ARPACK
• benchmodred

Cites Work

• Remark on the norm of random Hankel matrices
• A few remarks on the operator norm of random Toeplitz matrices
• Dynamic system identification. Experiment design and data analysis
• The fit of a sum of exponentials to noisy data
• Minimal state-space realization in linear system theory: An overview
• On the spectral norm of a random Toeplitz matrix
• Spectral measure of large random Hankel, Markov and Toeplitz matrices
• On the finite rank and finite-dimensional representation of bounded semi-infinite Hankel operators
• $\mathcal{H}_2$-Optimal Model Reduction with Higher-Order Poles
• Matrix Analysis
• An eigensystem realization algorithm for modal parameter identification and model reduction
• ARPACK Users' Guide
• Model Selection and Multimodel Inference
• A new look at the statistical model identification
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