Open Access
December 2007 Conditional density estimation in a regression setting
Sam Efromovich
Ann. Statist. 35(6): 2504-2535 (December 2007). DOI: 10.1214/009053607000000253


Regression problems are traditionally analyzed via univariate characteristics like the regression function, scale function and marginal density of regression errors. These characteristics are useful and informative whenever the association between the predictor and the response is relatively simple. More detailed information about the association can be provided by the conditional density of the response given the predictor. For the first time in the literature, this article develops the theory of minimax estimation of the conditional density for regression settings with fixed and random designs of predictors, bounded and unbounded responses and a vast set of anisotropic classes of conditional densities. The study of fixed design regression is of special interest and novelty because the known literature is devoted to the case of random predictors. For the aforementioned models, the paper suggests a universal adaptive estimator which (i) matches performance of an oracle that knows both an underlying model and an estimated conditional density; (ii) is sharp minimax over a vast class of anisotropic conditional densities; (iii) is at least rate minimax when the response is independent of the predictor and thus a bivariate conditional density becomes a univariate density; (iv) is adaptive to an underlying design (fixed or random) of predictors.


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Sam Efromovich. "Conditional density estimation in a regression setting." Ann. Statist. 35 (6) 2504 - 2535, December 2007.


Published: December 2007
First available in Project Euclid: 22 January 2008

zbMATH: 1129.62025
MathSciNet: MR2382656
Digital Object Identifier: 10.1214/009053607000000253

Primary: 62G07
Secondary: 62C05 , 62E20

Keywords: Adaptation , analytic and Sobolev densities , anisotropic class , finite and infinite support , fixed and random designs , lower bound , MISE , Oracle inequality , parametric , waste water treatment

Rights: Copyright © 2007 Institute of Mathematical Statistics

Vol.35 • No. 6 • December 2007
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