@InProceedings{Moddemeijer:1999:EASb,
  author =       "R. Moddemeijer",
  title =        "An efficient algorithm for selecting optimal
                 configurations of {AR}-coefficients",
  editor =       "A. Barb\'e et. al.",
  booktitle =    "Twentieth Symposium on Information Theory in the
                 Benelux",
  address =     "Haasrode (B)",
  month =        may # " 27-28 ",
  publisher =    "Werkgemeenschap Informatie- en Communicatietheorie, Enschede (NL)",
  year =         "1999",
  pages =        "189-196",
  url =          "http://www.cs.rug.nl/~rudy/papers/abstracts/RM9902.html",
  url =          "ftp://ftp.cs.rug.nl/pub/users/rudy/slides/RM9902.ps.gz",
  url =          "ftp://ftp.cs.rug.nl/pub/users/rudy/documents/RM9902.ps.gz",
  ISBN =         "90-71048-14-4",
  checked =      "R. Moddemeijer, rudy at cs.rug.nl, 31 May 1999",
  entered =      "R. Moddemeijer, rudy at cs.rug.nl, 17 March 1999",
  abstract =     "There exists an essential difference between the
                 correct Auto Regressive (AR) model and the optimal
                 AR-model. We try to find an optimal model balancing
                 between flexibility, using many AR-parameters, and low
                 variance, using only a few AR-parameters. We select an
                 optimal AR-parameter configuration consisting of zero
                 and non-zero parameters given a maximum AR-order. This
                 optimal configuration will be selected using a Modified
                 Information Criterion (MIC) which is closely related to
                 Akaike's criterion (AIC). This MIC allows an a priori
                 selection of the probability of estimating too many
                 parameters. We present the method and a verification by
                 simulations. The method is based on pivoting the
                 Hessian matrix by Gauss-Jordan pivots. As a result we
                 can now select an optimal parameter configuration with
                 an a priori probability of selecting a configuration
                 with a too large number of parameters given an a priori
                 selected maximum AR-order.",
}