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Title: Efficient parameter-estimating algorithms for symmetry-motivated models: Econometrics and beyond
Authors: Vladik Kreinovich
Anh H. Ly
Olga Kosheleva
Songsak Sriboonchitta
Keywords: Computer Science
Issue Date: 1-Jan-2018
Abstract: © 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.
ISSN: 1860949X
Appears in Collections:CMUL: Journal Articles

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