An Organizing Principle for Dynamic EstimationR. Kalaba and Leigh Tesfatsion () Staff General Research Papers from Iowa State University, Department of Economics Abstract: This study develops a general multicriteria Flexible Least Squares (FLS) 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 FLS framework is used to clarify both Bayesian and classical statistical procedures for treating potential model specification errors. Recently developed bicriteria specializations (FLS-TVLR, GFLS-ALS), explicitly designed to take model specification errors into account, are also reviewed. The latter specializations concretely demonstrate how the FLS framework can be used to construct estimation algorithms capable of handling disparate sources of information coherently and systematically, without forced scalarization. Annotated pointers to related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/flshome.htm Keywords: Dynamical models; nonlinear estimation; flexible least squares; Kalman filtering (search for similar items in EconPapers) Published in Journal of Optimization Theory and Applications, March 1990, Vol. 64, No. 3, pp. 445-470. There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: http://EconPapers.repec.org/RePEc:isu:genres:11194 Access Statistics for this paper More papers in Staff General Research Papers from Iowa State University, Department of Economics |
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