Nonparametric estimation by convex programming

Abstract

The problem we concentrate on is as follows: given (1) a convex compact set $X$ in ${\mathbb{R}}^n$, an affine mapping $x\mapsto A(x)$, a parametric family $\{p_{\mu}(\cdot)\}$ of probability densities and (2) $N$ i.i.d. observations of the random variable $\omega$, distributed with the density $p_{A(x)}(\cdot)$ for some (unknown) $x\in X$, estimate the value $g^Tx$ of a given linear form at $x$. For several families $\{p_{\mu}(\cdot)\}$ with no additional assumptions on $X$ and $A$, we develop computationally efficient estimation routines which are minimax optimal, within an absolute constant factor. We then apply these routines to recovering $x$ itself in the Euclidean norm.Comment: Published in at http://dx.doi.org/10.1214/08-AOS654 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org