Asymptotic equivalence of nonparametric autoregression and nonparametric regression
Grama, Ion G. ; Neumann, Michael H.
Ann. Statist., Tome 34 (2006) no. 1, p. 1701-1732 / Harvested from Project Euclid
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It is proved that nonparametric autoregression is asymptotically equivalent in the sense of Le Cam’s deficiency distance to nonparametric regression with random design as well as with regular nonrandom design.
Publié le : 2006-08-14
Classification:  Asymptotic equivalence,  deficiency distance,  Gaussian approximation,  nonparametric autoregression,  nonparametric regression,  62B15,  62G07,  62G20 
@article{1162567630,
     author = {Grama, Ion G. and Neumann, Michael H.},
     title = {Asymptotic equivalence of nonparametric autoregression and nonparametric regression},
     journal = {Ann. Statist.},
     volume = {34},
     number = {1},
     year = {2006},
     pages = { 1701-1732},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1162567630}
}

Grama, Ion G.; Neumann, Michael H. Asymptotic equivalence of nonparametric autoregression and nonparametric regression. Ann. Statist., Tome 34 (2006) no. 1, pp.  1701-1732. http://gdmltest.u-ga.fr/item/1162567630/