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• W2130166596 abstract "This paper proposes neural network based measures of predictability in conditional mean, and then uses them to construct nonlinear analogues to autocorrelograms and partial autocorrelograms. In contrast to other measures of nonlinear dependence that rely on nonparametric estimation of densities or multivariate integration, our autocorrelograms are simple to calculate and appear to work well in relatively small" @default.
• W2130166596 created "2016-06-24" @default.
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• W2130166596 date "2005-12-01" @default.
• W2130166596 modified "2023-09-25" @default.
• W2130166596 title "Nonlinear Correlograms and Partial Autocorrelograms*" @default.
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• W2130166596 doi "https://doi.org/10.1111/j.1468-0084.2005.00147.x" @default.
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