Currently displaying 1 – 2 of 2

Showing per page

Order by Relevance | Title | Year of publication

Adaptive density estimation under weak dependence

Irène Gannaz;  Olivier Wintenberger — 2010

ESAIM: Probability and Statistics

Assume that () is a real valued time series admitting a common marginal density with respect to Lebesgue's measure. [Donoho   (1996) 508–539] propose near-minimax estimators f ^ n based on thresholding wavelets to estimate f on a compact set in an independent and identically distributed setting. The aim of the present work is to extend these results to general weak dependent contexts. Weak dependence assumptions are expressed as decreasing bounds of covariance terms and are detailed...

Prediction of time series by statistical learning: general losses and fast rates

Pierre Alquier;  Xiaoyin Li;  Olivier Wintenberger — 2013

Dependence Modeling

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 for the...

Page 1

Download Results (CSV)