Minimax Lower Bound of k-Monotone Estimation in the Sup-norm

Author/Creator ORCID

Date

2019-04-18

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Citation of Original Publication

Teresa M. Lebair, Jinglai Shen, Minimax Lower Bound of k-Monotone Estimation in the Sup-norm, 2019 53rd Annual Conference on Information Sciences and Systems (CISS), DOI: 10.1109/CISS.2019.8692914

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Abstract

Belonging to the framework of shape constrained estimation, k-monotone estimation refers to the nonparametric estimation of univariate k-monotone functions, e.g., monotone and convex unctions. This paper develops minimax lower bounds for k-monotone regression problems under the sup-norm for general k by constructing a family of k-monotone piecewise polynomial functions (or hypotheses) belonging to suitable Hölder and Sobolev classes. After establishing that these hypotheses satisfy several properties, we employ results from general min-imax lower bound theory to obtain the desired k-monotone regression minimax lower bound. Implications and extensions are also discussed.