Adaptation Bounds for Confidence Bands under Self-Similarity

Timothy Armstrong

No 2146, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

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 (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2018-10
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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Working Paper: Adaptation Bounds for Confidence Bands under Self-Similarity (2019)
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