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On Local Trigonometric Regression Under Dependence

Beran, Jan ; Steffens, Britta ; Ghosh, Sucharita

Journal of time series analysis, 2018-07, Vol.39 (4), p.592-617 [Revista revisada por pares]

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Título:
On Local Trigonometric Regression Under Dependence
Autor: Beran, Jan ; Steffens, Britta ; Ghosh, Sucharita
Materias: Adaptive algorithms ; Computer simulation ; Convergence ; Density ; Dependence ; Estimators ; local trigonometric regression ; long‐range dependence ; Regression analysis ; seasonal smoothing ; Time series ; time series decomposition
Es parte de: Journal of time series analysis, 2018-07, Vol.39 (4), p.592-617
Descripción: We consider nonparametric estimation of an additive time series decomposition into a long‐term trend μ and a smoothly changing seasonal component S under general assumptions on the dependence structure of the residual process. The rate of convergence of local trigonometric regression estimators of S turns out to be unaffected by the dependence, even though the spectral density of the residual process has a pole at the origin. In contrast, the rate of convergence of nonparametric estimators of μ depends on the long‐memory parameter d. Therefore, in the presence of long‐range dependence, different bandwidths for estimating μ and S should be used. A data adaptive algorithm for optimal bandwidth choice is proposed. Simulations and data examples illustrate the results.
Editor: Oxford, UK: John Wiley & Sons, Ltd
Idioma: Inglés

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