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2018 Fast adaptive estimation of log-additive exponential models in Kullback-Leibler divergence
Cristina Butucea, Jean-François Delmas, Anne Dutfoy, Richard Fischer
Electron. J. Statist. 12(1): 1256-1298 (2018). DOI: 10.1214/18-EJS1413

Abstract

We study the problem of nonparametric estimation of probability density functions (pdf) 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 pdf’s appear in the random truncation model as the joint pdf of the 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 pdf’s whose logarithm belong 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.

Citation

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Cristina Butucea. Jean-François Delmas. Anne Dutfoy. Richard Fischer. "Fast adaptive estimation of log-additive exponential models in Kullback-Leibler divergence." Electron. J. Statist. 12 (1) 1256 - 1298, 2018. https://doi.org/10.1214/18-EJS1413

Information

Received: 1 May 2017; Published: 2018
First available in Project Euclid: 4 April 2018

zbMATH: 06864491
MathSciNet: MR3782583
Digital Object Identifier: 10.1214/18-EJS1413

Subjects:
Primary: 62G05, 62G07, 62G20

JOURNAL ARTICLE
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Vol.12 • No. 1 • 2018
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