# High-dimensional nonparametric density estimation via symmetry and shape constraints

## TL;DR

This paper introduces a scalable method for high-dimensional nonparametric density estimation under symmetry constraints, achieving optimal risk bounds and strong finite-sample performance.

## Contribution

It proposes the $K$-homothetic log-concave maximum likelihood estimator, providing risk bounds and adaptivity results for high-dimensional density estimation with symmetry assumptions.

## Key findings

- Risk bound of $O(n^{-4/5})$ independent of dimension
- Estimator adapts to special density forms for near-parametric rates
- Algorithms are efficient for large-scale high-dimensional data

## Abstract

We tackle the problem of high-dimensional nonparametric density estimation by taking the class of log-concave densities on $\mathbb{R}^p$ and incorporating within it symmetry assumptions, which facilitate scalable estimation algorithms and can mitigate the curse of dimensionality. Our main symmetry assumption is that the super-level sets of the density are $K$-homothetic (i.e. scalar multiples of a convex body $K \subseteq \mathbb{R}^p$). When $K$ is known, we prove that the $K$-homothetic log-concave maximum likelihood estimator based on $n$ independent observations from such a density has a worst-case risk bound with respect to, e.g., squared Hellinger loss, of $O(n^{-4/5})$, independent of $p$. Moreover, we show that the estimator is adaptive in the sense that if the data generating density admits a special form, then a nearly parametric rate may be attained. We also provide worst-case and adaptive risk bounds in cases where $K$ is only known up to a positive definite transformation, and where it is completely unknown and must be estimated nonparametrically. Our estimation algorithms are fast even when $n$ and $p$ are on the order of hundreds of thousands, and we illustrate the strong finite-sample performance of our methods on simulated data.