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A relation between the Akaike criterion and reliability of parameter estimates, with application to nonlinear autoregressive modelling of ictal EEG

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Abstract

The Akaike minimum information criterion provides a means to determine the appropriate number of lags in a linear autoregressive model of a time series. We show that the Akaike criterion is closely related to the reliability estimates of successively determined parameters of a linear autoregressive (LAR) model. A similar criterion may be applied to determine whether the addition of a nonlinear term to an LAR model provides a statistically significant improvement in the description of the time series. As an example, we use this method to identify quadratic contributions to a nonlinear autoregressive characterization of a typical 3/s spike and wave seizure discharge.

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Authors and Affiliations

  1. Department of Neurology and Neuroscience, The New York Hospital-Cornell Medical Center, 525 East Sixth-eighth Street, 10021, New York, NY

    Jonathan D. Victor & Annemarie Canel

  2. The Rockefeller University, 1230 York Avenue, New York, NY

    Jonathan D. Victor

Authors
  1. Jonathan D. Victor
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  2. Annemarie Canel
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Victor, J.D., Canel, A. A relation between the Akaike criterion and reliability of parameter estimates, with application to nonlinear autoregressive modelling of ictal EEG. Ann Biomed Eng 20, 167–180 (1992). https://doi.org/10.1007/BF02368518

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  • DOI: https://doi.org/10.1007/BF02368518

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