Digital spectral analysis: with applications
Digital spectral analysis: with applications
Estimation of parameters and eigenmodes of multivariate autoregressive models
ACM Transactions on Mathematical Software (TOMS)
ACM Transactions on Mathematical Software (TOMS)
Body sensor networks based sensor fusion for cardiovascular biosignal predictions
Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments
Order selection criteria for vector autoregressive models
Signal Processing
The Journal of Supercomputing
AIASABEBI'11 Proceedings of the 11th WSEAS international conference on Applied informatics and communications, and Proceedings of the 4th WSEAS International conference on Biomedical electronics and biomedical informatics, and Proceedings of the international conference on Computational engineering in systems applications
Statistical pitfalls in the comparison of multivariate causality measures for effective causality
Computers in Biology and Medicine
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Recently, a new estimator--Arfit--for multivariate (vector) autoregressive (MVAR) parameters has been proposed. Several other MVAR estimators (e.g. Levinson recursion, Burg-type Nuttall-Strand, etc.) were already well known in the field of signal processing.The various MVAR estimators have been implemented for Octave and Matlab. A method based on cross-validation and bootstrapping has been developed for comparing the various estimators. Thousand realizations of a MVAR(6)-process with 5 channels and a length of 1000 samples were generated. Each realization was separated into training and a test period. The training period was used to estimate the MVAR-parameters with each algorithm; the testing period was used to probe the accuracy of the estimates.For large sample sizes, the Burg-type algorithm and Arfit yielded similar results, the multivariate Levinson method was worse. For small sample sizes, the Burg-type Nuttall-Strand method was signifcantly better than multivariate Levinson, the Arfit estimates performed worst.In summary, the Nuttall Strand method (multivariate Burg) for estimating MVAR parameters yielded the best results. The implementation of the algorithms for Octave and Matlab has been made available on the world wide web.