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Optimal convergence rates in non-parametric regression with fractional time series errors

Optimal convergence rates in non-parametric regression with fractional time series errors

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FENG, Yuanhua, Jan BERAN, 2013. Optimal convergence rates in non-parametric regression with fractional time series errors. In: Journal of Time Series Analysis. 34(1), pp. 30-39. ISSN 0143-9782. eISSN 1467-9892

@article{Feng2013Optim-24965, title={Optimal convergence rates in non-parametric regression with fractional time series errors}, year={2013}, doi={10.1111/j.1467-9892.2012.00811.x}, number={1}, volume={34}, issn={0143-9782}, journal={Journal of Time Series Analysis}, pages={30--39}, author={Feng, Yuanhua and Beran, Jan} }

Consider the estimation of g(ν), the νth derivative of the mean function, in a fixed-design non-parametric regression model with stationary time series errors ξi. We assume that , ξi are obtained by applying an invertible linear filter to iid innovations, and the spectral density of ξi has the form as λ → 0 with constants cf > 0 and α ∈ (−1,1). Under regularity conditions, the optimal convergence rate of is shown to be with r = (1 − α)(k − ν)/(2k+1 − α). This rate is achieved by local polynomial fitting. Moreover, in spite of including long memory and antipersistence, the required conditions on the innovation distribution turn out to be the same as in non-parametric regression with iid errors. Beran, Jan Feng, Yuanhua Beran, Jan eng 2013-10-28T09:35:47Z 2013 Optimal convergence rates in non-parametric regression with fractional time series errors Journal of Time Series Analysis ; 34 (2013), 1. - S. 30-39 deposit-license 2013-10-28T09:35:47Z Feng, Yuanhua

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