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• W2038238162 abstract "We improve the least squares (LS) method for building models of a nonlinear dynamical system given finite time series which are contaminated by observational noise. When the noise level is low, the LS method gives good estimates for the parameters, however, the models selected as the best by information criteria often tend to be over-parameterized or even degenerate. We observe that the correct model is not selected as the best model despite belonging to the chosen model class. To overcome this, we propose a simple but very effective idea to use the LS method more appropriately. We apply the method for model selection. Numerical studies indicate that the method can be used to apply information criteria more effectively, and generally avoid over-fitting and model degeneracy." @default.
• W2038238162 created "2016-06-24" @default.
• W2038238162 creator A5001298193 @default.
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• W2038238162 date "2006-02-01" @default.
• W2038238162 modified "2023-09-23" @default.
• W2038238162 title "MODELING NONLINEAR TIME SERIES USING IMPROVED LEAST SQUARES METHOD" @default.
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• W2038238162 doi "https://doi.org/10.1142/s0218127406014927" @default.
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