Result: An organizing principle for dynamic estimation

Title:
An organizing principle for dynamic estimation
Authors:
Robert E. Kalaba, Leigh Tesfatsion
Source:
Journal of Optimization Theory and Applications. 64:445-470
Publisher Information:
Springer Science and Business Media LLC, 1990.
Publication Year:
1990
Subject Terms:
0209 industrial biotechnology, Estimation and detection in stochastic control theory, Management decision making, including multiple objectives, Computational methods in stochastic control, multicriteria framework, smoothing, 02 engineering and technology, Dynamic programming, Filtering in stochastic control theory, Applications of mathematical programming, Bayesian problems, characterization of Bayes procedures, 0202 electrical engineering, electronic engineering, information engineering, sequential estimation of process states, model misspecification, Multi-objective and goal programming
Document Type:
Academic journal Article
File Description:
application/xml
Language:
English
ISSN:
1573-2878
0022-3239
DOI:
10.1007/bf00939418
Access URL:
https://link.springer.com/article/10.1007%2FBF00939418
https://link.springer.com/content/pdf/10.1007/BF00939418.pdf
Rights:
Springer TDM
Accession Number:
edsair.doi.dedup.....0717df99bf98df58e94c80264617c25a
Database:
OpenAIRE

Further Information

This paper develops a general multicriteria framework for the sequential estimation of process states. Three well-known state estimation algorithms (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.