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The smoothing dichotomy in nonparametric regression under long‐memory errors
Author(s) -
Csörgő Sándor
Publication year - 2002
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/1467-9574.00188
Subject(s) - smoothing , nonparametric regression , estimator , mathematics , nonparametric statistics , kernel smoother , kernel regression , range (aeronautics) , regression , kernel (algebra) , gaussian , econometrics , statistics , asymptotic distribution , kernel method , computer science , artificial intelligence , pure mathematics , physics , quantum mechanics , radial basis function kernel , support vector machine , materials science , composite material
The phenomenon of smoothing dichotomy in random‐design nonparametric regression is exposed in nontechnical terms from two recent papers published jointly with Jan Mielniczuk. This concerns the asymptotic distribution of kernel estimators when the errors exhibit long‐range dependence, being instantaneous functions either of Gaussian sequences or of infinite‐order moving averages, depending on the amount of smoothing.