Internal analysis and optimization applied to parameter estimation under uncertainty

We present a methodology through exemplification to perform parameter estimation subject to possible factors of uncertainty. The underlying optimization problem is posed in the framework of the theory of interval-valued optimization. The implementation of numerical procedures required to achieve eff...

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Main Authors: Jose Daniel Gallego-Posada, Maria Eugenia Puerta-Yepes
Format: Article
Language:English
Published: Sociedade Brasileira de Matemática 2018-04-01
Series:Boletim da Sociedade Paranaense de Matemática
Subjects:
Online Access:https://periodicos.uem.br/ojs/index.php/BSocParanMat/article/view/29309
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author Jose Daniel Gallego-Posada
Maria Eugenia Puerta-Yepes
author_facet Jose Daniel Gallego-Posada
Maria Eugenia Puerta-Yepes
author_sort Jose Daniel Gallego-Posada
collection DOAJ
description We present a methodology through exemplification to perform parameter estimation subject to possible factors of uncertainty. The underlying optimization problem is posed in the framework of the theory of interval-valued optimization. The implementation of numerical procedures required to achieve efficient solutions implied the use of the $\ell_1$ norm instead of usual $\ell_2$ regression. Finally, an implementation using real data was performed, demonstrating the ability of interval analysis to encapsulate uncertainty while facing non-trivial parameter estimation problems.
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spelling doaj.art-eeb02cb7bbf54bfe9a7d2c4431f406e92023-11-08T20:10:36ZengSociedade Brasileira de MatemáticaBoletim da Sociedade Paranaense de Matemática0037-87122175-11882018-04-0136210.5269/bspm.v36i2.2930915439Internal analysis and optimization applied to parameter estimation under uncertaintyJose Daniel Gallego-Posada0Maria Eugenia Puerta-Yepes1Universidad EAFITUniversidad EAFITWe present a methodology through exemplification to perform parameter estimation subject to possible factors of uncertainty. The underlying optimization problem is posed in the framework of the theory of interval-valued optimization. The implementation of numerical procedures required to achieve efficient solutions implied the use of the $\ell_1$ norm instead of usual $\ell_2$ regression. Finally, an implementation using real data was performed, demonstrating the ability of interval analysis to encapsulate uncertainty while facing non-trivial parameter estimation problems.https://periodicos.uem.br/ojs/index.php/BSocParanMat/article/view/29309optimizationinterval-valued analysisparameter estimation and inverse problems
spellingShingle Jose Daniel Gallego-Posada
Maria Eugenia Puerta-Yepes
Internal analysis and optimization applied to parameter estimation under uncertainty
Boletim da Sociedade Paranaense de Matemática
optimization
interval-valued analysis
parameter estimation and inverse problems
title Internal analysis and optimization applied to parameter estimation under uncertainty
title_full Internal analysis and optimization applied to parameter estimation under uncertainty
title_fullStr Internal analysis and optimization applied to parameter estimation under uncertainty
title_full_unstemmed Internal analysis and optimization applied to parameter estimation under uncertainty
title_short Internal analysis and optimization applied to parameter estimation under uncertainty
title_sort internal analysis and optimization applied to parameter estimation under uncertainty
topic optimization
interval-valued analysis
parameter estimation and inverse problems
url https://periodicos.uem.br/ojs/index.php/BSocParanMat/article/view/29309
work_keys_str_mv AT josedanielgallegoposada internalanalysisandoptimizationappliedtoparameterestimationunderuncertainty
AT mariaeugeniapuertayepes internalanalysisandoptimizationappliedtoparameterestimationunderuncertainty