Identification of low rank vector processes

Alpago, 2018, Identification of sparse reciprocal graphical models, IEEE Control Systems Letters, 2, 659, 10.1109/LCSYS.2018.2845943 Alpago, 2021, Data-driven link prediction over graphical models, IEEE Transactions on Automatic Control Basu, 2019, Low rank and structured modeling of high-dimensional vector autoregressions, IEEE Transactions on Singnal Processing, 67, 1207, 10.1109/TSP.2018.2887401 Bauer, 1955, Ein direktes iterationsverfahren zur hurwitz zerlegung eines polynoms, Archiv der Elektrischen Übertragung, 9, 285 Bazanella, 2017, Identifiability of dynamical networks: which nodes need be measured?, 5870 Bottegal, 2015, Modeling complex systems by generalized factor analysis, IEEE Transactions on Automatic Control, 60, 759, 10.1109/TAC.2014.2357913 Caines, 1988 Caines, 1975, Feedback between stationary stochastic processes, IEEE Transactions on Automatic Control, AC-20, 498, 10.1109/TAC.1975.1101008 Cao, W., Lindquist, A., & Picci, G. (2020). Spectral rank, feedback, causality and the indirect method for CARMA identification. In Proceedings of 59th IEEE conference on decision and control CDC, (pp. 4299–4305). Jeju, Korea (South). Cao, 2021 Chiuso, 2012, A Bayesian approach to sparse dynamic network identification, Automatica, 48, 1553, 10.1016/j.automatica.2012.05.054 Ciccone, 2020, Learning latent variable dynamic graphical models by confidence sets selection, IEEE Transactions on Automatic Control, 65, 5130, 10.1109/TAC.2020.2970409 Crescente, F., Falconi, L., Rozzi, F., Ferrante, A., & Zorzi, M. (2020). Learning AR factor models. In Proceedings of 59th IEEE conference on decision and control CDC, (pp. 274–270). Jeju, Korea (South). Deistler, 2019, Singular ARMA systems: A structural theory, Numerical Algebra, Control and Optimization, 9, 383, 10.3934/naco.2019025 Deistler, 2010, Generalized linear dynamic factor models: An approach via singular autoregressions, European Journal of Control, 16, 211, 10.3166/ejc.16.211-224 Doyle, 1992 Ferrante, 2022 Fuhrmann, 1981 Georgiou, 2019, Dynamic relations in sampled processes, Control Systems Letters, 3, 144, 10.1109/LCSYS.2018.2859481 Gevers, 1981, Representation of jointly stationary feedback free processes, International Journal of Control, 33, 777, 10.1080/00207178108922956 Gustavsson, 1977, Identification of processes in closed loop–identifiability and accuracy aspects, Automatica, 13, 59, 10.1016/0005-1098(77)90009-7 Hidalgo, 2009, A dynamic network approach for the study of human phenotypes, PLoS Computational Biology, 5, 10.1371/journal.pcbi.1000353 Kailath, 1980 Lichota, 2019, Frequency responses identification from multi-axis maneuver with simultaneous multisine inputs, Journal of Guidance, Control, and Dynamics, 42, 2550, 10.2514/1.G004346 Lindquist, 2015 Lippi, 2022, High-dimensional dynamic factor models: A selective survey and lines of future research, Econometrics and Statistics, 1 Ljung, 2002 Oară, 1999, Minimal degree coprime factorization of rational matrices, SIAM Journal on Matrix Analysis and Applications, 21, 245, 10.1137/S0895479898339979 Picci, 2021, Modeling and identification of low rank vector processes, 631 Remple, 2006 Rissanen, 1973, Algorithms for triangular decomposition of block Hankel and Toeplitz matrices with applications to factoring positive matrix Polynomials, Journal of Mathematics of Computation, 27, 147, 10.1090/S0025-5718-1973-0329235-5 Söderström, 1989 Van den Hof, P., Weerts, H., & Dankers, A. (2017). Prediction error identification with rank-reduced output noise. In Proceedings of 2017 American control conference (pp. 382–387). Seattle, USA. Weerts, 2018, Identifiability of linear dynamic networks, Automatica, 89, 247, 10.1016/j.automatica.2017.12.013 Weerts, 2018, Prediction error identification of linear dynamic networks with rank-reduced noise, Automatica, 98, 256, 10.1016/j.automatica.2018.09.033 Yuan, 2011, Robust dynamical network structure reconstruction, Automatica, 47, 1230, 10.1016/j.automatica.2011.03.008 Zhou, 1995 Zorzi, 2016, AR identification of latent-variable graphical models, IEEE Transactions on Automatic Control, 61, 2327, 10.1109/TAC.2015.2491678