A Data-Driven McMillan Degree Lower Bound
From MaRDI portal
Publication:5146675
DOI10.1137/18M1194481zbMath1456.37098arXiv1803.00043OpenAlexW3097338623MaRDI QIDQ5146675
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
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)
Uses Software
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
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: A Data-Driven McMillan Degree Lower Bound
