A new Kalman filter approach for the estimation of high-dimensional time-variant multivariate AR models and its application in analysis of laser-evoked brain potentials

@article{Milde2010ANK,
  title={A new Kalman filter approach for the estimation of high-dimensional time-variant multivariate AR models and its application in analysis of laser-evoked brain potentials},
  author={Thomas Milde and Lutz Leistritz and Laura Astolfi and Wolfgang H. R. Miltner and Thomas Weiss and Fabio Babiloni and Herbert Witte},
  journal={NeuroImage},
  year={2010},
  volume={50 3},
  pages={960-9}
}
In this methodological study we present a new version of a Kalman filter technique to estimate high-dimensional time-variant (tv) multivariate autoregressive (tvMVAR) models. It is based on an extension of the state-space model for a multivariate time series to a matrix-state-space model for multi-trial multivariate time series. The result is a general linear Kalman filter (GLKF). The GLKF enables a tvMVAR model estimation which was applied for interaction analysis of simulated data and high… CONTINUE READING
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Applied optimal estimation

  • A. Gelb
  • 1974
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