CFDP 2146R

Adaptation Bounds for Confidence Bands under Self-Similarity

Author(s): 

Publication Date: October 2018

Revision Date: July 2019

Pages: 33

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.

Keywords: Adaptation, Nonparametric inference, Self-similarity

JEL Classification Codes: C14

JEL Classification Codes: C14

See CFDP Version(s): CFDP 2146