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• W2261141559 abstract "High-dimensional time-series data are becoming increasingly abundant across a wide variety of domains, spanning economics, neuroscience, particle physics, and cosmology. Fitting statistical models to such data, to enable parameter estimation and time-series prediction, is an important computational primitive. Existing methods, however, are unable to cope with the high-dimensional nature of these problems, due to both computational and statistical reasons. We mitigate both kinds of issues via proposing an M-estimator for Reduced-rank System IDentification (MR. SID). A combination of low-rank approximations, L-1 and L-2 penalties, and some numerical linear algebra tricks, yields an estimator that is computationally efficient and numerically stable. Simulations and real data examples demonstrate the utility of this approach in a variety of problems. In particular, we demonstrate that MR. SID can estimate spatial filters, connectivity graphs, and time-courses from native resolution functional magnetic resonance imaging data. Other applications and extensions are immediately available, as our approach is a generalization of the classical Kalman Filter-Smoother Expectation-Maximization algorithm." @default.
• W2261141559 created "2016-06-24" @default.
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• W2261141559 date "2015-09-14" @default.
• W2261141559 modified "2023-09-27" @default.
• W2261141559 title "An M-Estimator for Reduced-Rank High-Dimensional Linear Dynamical System Identification" @default.
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