Density estimation using cellular binary trees and an application to monotone densities

Luc Devroye; Jad Hamdan

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arXiv · 2203.08006

Density estimation using cellular binary trees and an application to monotone densities

Luc Devroye·Jad Hamdan

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Abstract

Consider a density $f$ on $[0,1]$ that must be estimated from an i.i.d. sample $X_1,...,X_n$ drawn from $f$. In this note, we study binary-tree-based histogram estimates that use recursive splitting of intervals. If the decision to split an interval is a (possibly randomized) function of the number of data points in the interval only, then we speak of an estimate of complexity one. We exhibit a universally consistent estimate of complexity one. If the decision to split is a function of the cardinalities of k equal-length sub-intervals, then we speak of an estimate of complexity k. We propose an estimate of complexity two that can estimate any bounded monotone density on $[0,1]$ with optimal expected total variation error $O(n^{-1/3})$.

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Luc Devroye, Jad Hamdan. 2022-03-15. Density estimation using cellular binary trees and an application to monotone densities. https://arxiv.org/abs/2203.08006

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