In recent years, log-concave density estimation via maximum likelihood estimation has
emerged as a fascinating alternative to traditional nonparametric smoothing techniques …

Weak Convergence Page 1 1.3 Weak Convergence In this section IDl and IE are metric spaces
with metrics d and e, respectively. The set of all continuous, bounded functions f: IDl 1--+ IR is …

This book treats the latest developments in the theory of order-restricted inference, with
special attention to nonparametric methods and algorithmic aspects. Among the topics …

The field of productive efficiency analysis is currently divided between two main paradigms:
the deterministic, nonparametric Data Envelopment Analysis (DEA) and the parametric …

This book provides an accessible collection of techniques for analyzing nonparametric and
semiparametric regression models. Worked examples include estimation of Engel curves …

Estimation of mixture densities for the classical Gaussian compound decision problem and
their associated (empirical) Bayes rules is considered from two new perspectives. The first …

We consider the problem of estimating the proportion of true null hypotheses, π 0, in a
multiple-hypothesis set-up. The tests are based on observed p-values. We first review …

We examine a nonparametric least‐squares regression model that endogenously selects
the functional form of the regression function from the family of continuous, monotonic …

Summary Let X 1,…, X n be independent and identically distributed random vectors with a
(Lebesgue) density f. We first prove that, with probability 1, there is a unique log-concave …

Nonparametric least squares estimation of a multivariate convex regression function Page 1
The Annals of Statistics 2011, Vol. 39, No. 3, 1633–1657 DOI: 10.1214/10-AOS852 © Institute …