Document Type
Discussion Paper
Publication Date
10-1-2018
CFDP Number
2146R
CFDP Revision Date
July 1, 2019
CFDP Pages
33
Journal of Economic Literature (JEL) Code(s)
C14
Abstract
We derive bounds on the scope for a confidence band to adapt to the unknown regularity of a nonparametric function that is observed with noise, such as a regression function or density, under the self-similarity condition proposed by Gine and Nickl (2010). We find that adaptation can only be achieved up to a term that depends on the choice of the constant used to define self-similarity, and that this term becomes arbitrarily large for conservative choices of the self-similarity constant. We construct a confidence band that achieves this bound, up to a constant term that does not depend on the self-similarity constant. Our results suggest that care must be taken in choosing and interpreting the constant that defines self-similarity, since the dependence of adaptive confidence bands on this constant cannot be made to disappear asymptotically.
Recommended Citation
Armstrong, Timothy B., "Adaptation Bounds for Confidence Bands under Self-Similarity" (2018). Cowles Foundation Discussion Papers. 120.
https://elischolar.library.yale.edu/cowles-discussion-paper-series/120