Abstract:
Given output data of a stationary stochastic process estimates of the covariances parameters can be obtained. These estimates can be used to determine ARMA models to appr...Show MoreMetadata
Abstract:
Given output data of a stationary stochastic process estimates of the covariances parameters can be obtained. These estimates can be used to determine ARMA models to approximately fit the data by matching the covariances exactly. However, the estimates of the covariances may contain large errors, especially if they are determined from short data sequences, and thus it makes sense to match the covariances only in an approximative way. Here we consider a convex method for solving an approximative covariance interpolation problem while maximizing the entropy and penalize the quadratic deviation from the nominal covariances.
Published in: 2007 46th IEEE Conference on Decision and Control
Date of Conference: 12-14 December 2007
Date Added to IEEE Xplore: 21 January 2008
ISBN Information:
Print ISSN: 0191-2216