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An organizing principle for dynamic estimation

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Abstract

This paper develops a general multicriteria framework for the sequential estimation of process states. Three well-known state estimation algorithms (the Viterbi, Larson-Peschon, and Kalman filters) are derived as monocriterion specializations. The multicriteria estimation framework is used to clarify both Bayesian and classical statistical procedures for treating potential model misspecification. A recently developed bicriteria specialization (flexible least cost), explicitly designed to take specification errors into account, is also reviewed. The latter application suggests how the multicriteria framework might be used to construct estimation algorithms capable of handling disparate sources of information coherently and systematically, without forced scalarization.

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Authors and Affiliations

  1. University of Southern California, Los Angeles, California

    R. Kalaba (Professor of Electrical and Biomedical Engineering) & L. Tesfatsion (Professor of Economics)

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  1. R. Kalaba
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  2. L. Tesfatsion
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Additional information

This paper is a revised version of Ref. 1. The authors are grateful to D. Hendry, R. Huss, H. Quirmbach, and J. Veitch for helpful comments and suggestions.

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Kalaba, R., Tesfatsion, L. An organizing principle for dynamic estimation. J Optim Theory Appl 64, 445–470 (1990). https://doi.org/10.1007/BF00939418

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