Estimation of parameters and eigenmodes of multivariate autoregressive models

By Arnold Neumaier, Tapio Schneider. ACM Transactions on Mathematical Software, 2001

569 citations recorded by the source corpus.

Subfields: Applied Mathematics, Environmental Engineering, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/d9b5f6e65a3db8ea96220d0578a88a22ab3f19d6

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In Applied Mathematics

In Environmental Engineering

In Nuclear and High Energy Physics

Citations in the corpus

SPLIDHOM: A Method for Homogenization of Daily Temperature Observations (2011, methodology): “; N 0 0 , s2 1 r r 1 ; that is, the noise term fU1, U2g of the process follows a centered bivariate normal distribution, with the correlation between «1 and «2 being controlled by parameter r.”
A critical assessment of connectivity measures for EEG data: A simulation study (2013, methodology): “The ARmodel underlying the computation of PDC is estimated using the ARFIT package (Neumaier and Schneider, 2001).”
On How Network Architecture Determines the Dominant Patterns of Spontaneous Neural Activity (2008, background): “However, in the case of the stochastic system (3) considered here, stability is guaranteed if all the eigenvalues of A have magnitude less than unity [37,38], which means that the spontaneous activity cannot grow infinitely in response to noise, but will remain fluctuating around its mean value.”
Dynamical statistical shape priors for level set-based tracking (2006, methodology): “We applied a stepwise least squares algorithm proposed in [20].”
HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity (2013, unknown): “The coefficients of the MAR model of the desired order are estimated by means of a simplified version of the ARfit toolbox (Neumaier and Schneider 2001; Schneider and Neumaier 2001).”
Vision based fire detection (2004, methodology): “We estimate the parameters of our AR(1) model using Neumaier and Schneider’s algorithms [8].”
Fluctuations in functional connectivity associated with interictal epileptiform discharges (IEDs) in intracranial EEG (2021, unknown): “We then fit a smooth spline function to each power spectral density using fit() in MATLAB with a smoothing parameter of 0.01 Hz.”
Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior (2019, background): “One option is to use effective connectivity [70, 78] to solve for a network of connections that best predicts the evolution of brain states in time.”