On the theorem of Gauss-Kusmin-Lévy and a Frobenius-type theorem for function spaces
Eduard Wirsing (1974)
Acta Arithmetica
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On the theorem of Gauss-Kusmin-Lévy and a Frobenius-type theorem for function spaces
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We establish rates of convergences in statistical learning for time series forecasting. Using the PAC-Bayesian approach, slow rates of convergence √ d/n for the Gibbs estimator under the absolute loss were given in a previous work [7], where n is the sample size and d the dimension of the set of predictors. Under the same weak dependence conditions, we extend this result to any convex Lipschitz loss function. We also identify a condition on the parameter space that ensures similar rates...
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