Fast adaptive estimation of log-additive exponential models in Kullback-Leibler divergence

TL;DR

This paper introduces a fast, adaptive nonparametric estimation method for log-additive exponential models in density functions, achieving rapid convergence rates in Kullback-Leibler divergence, especially for densities with Sobolev regularity.

Contribution

It proposes a novel basis function approximation approach for density estimation and develops an adaptive procedure using convex aggregation to handle unknown regularity.

Findings

Method achieves fast convergence rates in probability.

Adaptive procedure performs well when regularity is unknown.

Simulation studies confirm effectiveness of the proposed approach.

Abstract

We study the problem of nonparametric estimation of density functions with a product form on the domain $\triangle=\{( x_1, \ldots, x_d)\in \mathbb{R}^d, 0\leq x_1\leq \dots \leq x_d \leq 1\}$. Such densities appear in the random truncation model as the joint density function of observations. They are also obtained as maximum entropy distributions of order statistics with given marginals. We propose an estimation method based on the approximation of the logarithm of the density by a carefully chosen family of basis functions. We show that the method achieves a fast convergence rate in probability with respect to the Kullback-Leibler divergence for densities whose logarithm belongs to a Sobolev function class with known regularity. In the case when the regularity is unknown, we propose an estimation procedure using convex aggregation of the log-densities to obtain adaptability. The performance of this method is illustrated in a simulation study.