Optimal kernel selection for density estimation
M Lerasle (JAD), N Magalh\~A{\pounds}es (UPMC), P Reynaud-Bouret (JAD)
TL;DR
This paper introduces new kernel selection rules for density estimation using penalized least-squares criteria, providing optimal oracle inequalities and exploring minimal penalty concepts.
Contribution
It offers novel kernel selection methods with theoretical guarantees and analyzes minimal penalty strategies in density estimation.
Findings
Derived optimal oracle inequalities for kernel selection
Proposed new penalized least-squares criteria
Investigated minimal penalty in kernel density estimation
Abstract
We provide new general kernel selection rules thanks to penalized least-squares criteria. We derive optimal oracle inequalities using adequate concentration tools. We also investigate the problem of minimal penalty as described in [BM07].
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Taxonomy
TopicsLiver Disease Diagnosis and Treatment · Statistical Methods and Inference · Control Systems and Identification