Asymptotic equivalence of nonparametric autoregression and nonparametric regression
Ion G. Grama and Michael H. Neumann
Source: Ann. Statist. Volume 34, Number 4 (2006), 1701-1732.
Abstract
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.
Primary Subjects: 62B15
Secondary Subjects: 62G07, 62G20
Keywords: Asymptotic equivalence; deficiency distance; Gaussian approximation; nonparametric autoregression; nonparametric regression
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Permanent link to this document: http://projecteuclid.org/euclid.aos/1162567630
Digital Object Identifier: doi:10.1214/009053606000000560
Mathematical Reviews number (MathSciNet):
MR2283714
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