Efficient Parameter-Estimating Algorithms for Symmetry-Motivated Models: Econometrics and Beyond

  • Vladik Kreinovich
  • Anh H. Ly
  • Olga Kosheleva
  • Songsak Sriboonchitta
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 760)

Abstract

It is known that symmetry ideas can explain the empirical success of many non-linear models. This explanation makes these models theoretically justified and thus, more reliable. However, the models remain non-linear and thus, identification or the model’s parameters based on the observations remains a computationally expensive nonlinear optimization problem. In this paper, we show that symmetry ideas can not only help to select and justify a nonlinear model, they can also help us design computationally efficient almost-linear algorithms for identifying the model’s parameters.

Notes

Acknowledgments

We acknowledge the partial support of the Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Thailand. This work was also supported in part by the National Science Foundation grant HRD-1242122 (Cyber-ShARE Center of Excellence).

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Vladik Kreinovich
    • 1
  • Anh H. Ly
    • 2
  • Olga Kosheleva
    • 1
  • Songsak Sriboonchitta
    • 3
  1. 1.University of Texas at El PasoEl PasoUSA
  2. 2.Banking University of Ho Chi Minh CityHo Ch Minh CityVietnam
  3. 3.Faculty of EconomicsChiang Mai UniversityChiang MaiThailand

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