WIAS Preprint No. 174, (1995)

Asymptotic Equivalence of Density Estimation and Gaussian White Noise



Authors

  • Nussbaum, Michael

2010 Mathematics Subject Classification

  • 62G07 62B15 62G20

Keywords

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

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 Δ 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 (Brown and Low, 1993). We consider the analogous problem for the experiment given by n i. i. d. observations having density ƒ on the unit interval. Our basic result concerns the parameter space of densities which are in a Hölder ball with exponent α > 1/2 and and which are uniformly bounded away from zero. We show that an i. i. d. sample of size n with density ƒ is globally asymptotically equivalent to a white noise experiment with drift ƒ 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 ƒ and log ƒ are also considered, allowing for various "automatic" asymptotic risk bounds in the i. i. d. model from white noise. As first applications we discuss exact constants for L2 and Hellinger loss.

Appeared in

  • Ann. Statist. 24 (1996) No. 6 pp. 2399--2430

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