Diffusion approximation for nonparametric autoregression

被引：16

作者：

Milstein, G ^{[1
]}

Nussbaum, M ^{[1
]}

机构：

[1] Karl Weierstrass Inst Math, D-10117 Berlin, Germany

来源：

PROBABILITY THEORY AND RELATED FIELDS | 1998年 / 112卷 / 04期

关键词：

nonparametric experiments; deficiency distance; likelihood ratio process; stochastic differential equation; autoregression; diffusion sampling; asymptotic sufficiency;

D O I：

10.1007/s004400050199

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

A nonparametric statistical model of small diffusion type is compared with its discretization by a stochastic Euler difference scheme. It is shown that the discrete and continuous models are asymptotically equivalent in the sense of Le Cam's deficiency distance for statistical experiments, when the discretization step decreases with the noise intensity epsilon.

引用

页码：535 / 543

页数：9

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