2024/05/26 by Galatia Cleanthous, Cleanthous, Galatia, Athanasios G. Georgiadis +3
Mathematics · #62G05 #62G20 #Advanced Research in Science and Engineering #FOS: Mathematics #Mathematical Approximation and Integration #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2405.16515
openalex publication_date 2024/05/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We deal with the problem of the adaptive estimation of the \mathbbL2-norm of a probability density on ℝd, d≥ 1, from independent observations. The unknown density is assumed to be uniformly bounded and to belong to the union of balls in the isotropic/anisotropic Nikolskii's spaces. We will show that the optimally adaptive estimators over the collection of considered functional classes do no exist. Also, in the framework of an abstract density model we present several generic lower bounds related to the adaptive estimation of an arbitrary functional of a probability density. These results having independent interest have no analogue in the existing literature. In the companion paper Cleanthous et al (2024) we prove that established lower bounds are tight and provide with explicit construction of adaptive estimators of \mathbbL2-norm of the density.