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arXiv 1503.05019math.PR
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密度估计和高斯白噪声的渐近等价性:一个推广

Asymptotic equivalence for density estimation and gaussian white noise: An extension

Ester Mariucci

摘要:

本文的目的是提出一个著名的渐近等价性之间的密度估计实验和高斯白噪声模型的扩展。我们的扩展包括扩大非参数类的容许密度。更确切地说,我们提出了一种方法,允许密度定义在R的任何子区间,也考虑了一些不连续或无界密度(只要不连续和无界模式是某种先验已知的)。我们将采用的等效概念是统计模型之间的Le Cam距离。结果是建设性的:所有的渐近等价建立通过构造显式马尔可夫核。

英文摘要:

The aim of this paper is to present an extension of the well-known as-ymptotic equivalence between density estimation experiments and a Gaussian white noise model. Our extension consists in enlarging the nonparametric class of the admissible densities. More precisely, we propose a way to allow densities defined on any subinterval of R, and also some discontinuous or unbounded densities are considered (so long as the discontinuity and unboundedness patterns are somehow known a priori). The concept of equivalence that we shall adopt is in the sense of the Le Cam distance between statistical models. The results are constructive: all the asymptotic equivalences are established by constructing explicit Markov kernels.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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