The Annals of Statistics

Asymptotic equivalence of density estimation and Gaussian white noise

Michael Nussbaum

Full-text: Open access

Abstract

Signal recovery in Gaussian white noise with variance tending to zero has served for some time as a representative model for nonparametric curve estimation, having all the essential traits in a pure form. The equivalence has mostly been stated informally, but an approximation in the sense of Le Cam's deficiency distance $\Delta$ would make it precise. The models are then asymptotically equivalent for all purposes of statistical decision with bounded loss. In nonparametrics, a first result of this kind has recently been established for Gaussian regression. We consider the analogous problem for the experiment given by n i.i.d. observations having density f on the unit interval. Our basic result concerns the parameter space of densities which are in a Hölder ball with exponent $\alpha > 1/2$ and which are uniformly bounded away from zero. We show that an i. i. d. sample of size n with density f is globally asymptotically equivalent to a white noise experiment with drift $f^{1/2}$ and variance $1/4 n^{-1}$. This represents a nonparametric analog of Le Cam's heteroscedastic Gaussian approximation in the finite dimensional case. The proof utilizes empirical process techniques related to the Hungarian construction. White noise models on f and log f are also considered, allowing for various "automatic" asymptotic risk bounds in the i.i.d. model from white noise.

Article information

Source
Ann. Statist. Volume 24, Number 6 (1996), 2399-2430.

Dates
First available in Project Euclid: 16 September 2002

Permanent link to this document
http://projecteuclid.org/euclid.aos/1032181160

Digital Object Identifier
doi:10.1214/aos/1032181160

Mathematical Reviews number (MathSciNet)
MR1425959

Zentralblatt MATH identifier
0867.62035

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

Keywords
Nonparametric experiments deficiency distance likelihood process Hungarian construction asymptotic minimax risk curve estimation

Citation

Nussbaum, Michael. Asymptotic equivalence of density estimation and Gaussian white noise. Ann. Statist. 24 (1996), no. 6, 2399--2430. doi:10.1214/aos/1032181160. http://projecteuclid.org/euclid.aos/1032181160.


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