The Annals of Statistics

Asymptotic equivalence of nonparametric autoregression and nonparametric regression

Ion G. Grama and Michael H. Neumann

Full-text: Open access

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.

Article information

Source
Ann. Statist., Volume 34, Number 4 (2006), 1701-1732.

Dates
First available in Project Euclid: 3 November 2006

Permanent link to this document
https://projecteuclid.org/euclid.aos/1162567630

Digital Object Identifier
doi:10.1214/009053606000000560

Mathematical Reviews number (MathSciNet)
MR2283714

Zentralblatt MATH identifier
1246.62105

Subjects
Primary: 62B15: Theory of statistical experiments
Secondary: 62G07: Density estimation 62G20: Asymptotic properties

Keywords
Asymptotic equivalence deficiency distance Gaussian approximation nonparametric autoregression nonparametric regression

Citation

Grama, Ion G.; Neumann, Michael H. Asymptotic equivalence of nonparametric autoregression and nonparametric regression. Ann. Statist. 34 (2006), no. 4, 1701--1732. doi:10.1214/009053606000000560. https://projecteuclid.org/euclid.aos/1162567630


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