Efficient parameter-estimating algorithms for symmetry-motivated models: Econometrics and beyond
© 2018, Springer International Publishing AG. 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’...
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th-cmuir.6653943832-439232018-01-24T04:15:16Z Efficient parameter-estimating algorithms for symmetry-motivated models: Econometrics and beyond Vladik Kreinovich Anh H. Ly Olga Kosheleva Songsak Sriboonchitta © 2018, Springer International Publishing AG. 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. 2018-01-24T04:15:16Z 2018-01-24T04:15:16Z 2018-01-01 Book Series 1860949X 2-s2.0-85038827524 10.1007/978-3-319-73150-6_10 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85038827524&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/43923 |
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© 2018, Springer International Publishing AG. 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. |
| format |
Book Series |
| author |
Vladik Kreinovich Anh H. Ly Olga Kosheleva Songsak Sriboonchitta |
| spellingShingle |
Vladik Kreinovich Anh H. Ly Olga Kosheleva Songsak Sriboonchitta Efficient parameter-estimating algorithms for symmetry-motivated models: Econometrics and beyond |
| author_facet |
Vladik Kreinovich Anh H. Ly Olga Kosheleva Songsak Sriboonchitta |
| author_sort |
Vladik Kreinovich |
| title |
Efficient parameter-estimating algorithms for symmetry-motivated models: Econometrics and beyond |
| title_short |
Efficient parameter-estimating algorithms for symmetry-motivated models: Econometrics and beyond |
| title_full |
Efficient parameter-estimating algorithms for symmetry-motivated models: Econometrics and beyond |
| title_fullStr |
Efficient parameter-estimating algorithms for symmetry-motivated models: Econometrics and beyond |
| title_full_unstemmed |
Efficient parameter-estimating algorithms for symmetry-motivated models: Econometrics and beyond |
| title_sort |
efficient parameter-estimating algorithms for symmetry-motivated models: econometrics and beyond |
| publishDate |
2018 |
| url |
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85038827524&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/43923 |
| _version_ |
1681422463802540032 |