The Le Cam distance between density estimation, Poisson processes and Gaussian white noise
File(s)AsymptoticEquivalence.pdf (540.66 KB)
Accepted version
OA Location
Author(s)
Ray, Kolyan
Schmidt-Hieber, Johannes
Type
Journal Article
Abstract
It is well known that density estimation on the unit interval is asymptotically equivalent to a Gaussian white noise experiment, provided the densities have Hölder smoothness larger than 1/2 and are uniformly bounded away from zero. We derive matching lower and constructive upper bounds for the Le Cam deficiencies between these experiments, with explicit dependence on both the sample size and the size of the densities in the parameter space. As a consequence, we derive sharp conditions on how small the densities can be for asymptotic equivalence to hold. The related case of Poisson intensity estimation is also treated.
Date Issued
2018-09-05
Date Acceptance
2018-05-29
Citation
Mathematical Statistics and Learning, 2018, 1 (2), pp.101-170
ISSN
2520-2316
Publisher
European Mathematical Society Publishing House
Start Page
101
End Page
170
Journal / Book Title
Mathematical Statistics and Learning
Volume
1
Issue
2
Copyright Statement
© 2018 EMS Publishing House. All rights reserved.
Identifier
https://www.ems-ph.org/journals/show_abstract.php?issn=2520-2316&vol=1&iss=2&rank=1
Publication Status
Published online
Date Publish Online
2018-09-05