Often researchers find parametric models restrictive and sensitive to deviations from the
parametric specifications; semi-nonparametric models are more flexible and robust, but lead …

We review estimation in interval censoring models, including nonparametric estimation of a
distribution function and estimation of regression models. In the nonparametric setting, we …

Rényi divergence is related to Rényi entropy much like Kullback-Leibler divergence is
related to Shannon's entropy, and comes up in many settings. It was introduced by Rényi as …

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 …

The theory of empirical processes provides valuable tools for the development of asymptotic
theory in (nonparametric) statistical models, and makes it possible to give a unified treatment …

The mathematical theory of machine learning not only explains the current algorithms but
can also motivate principled approaches for the future. This self-contained textbook …

We show that semiparametric profile likelihoods, where the nuisance parameter has been
profiled out, behave like ordinary likelihoods in that they have a quadratic expansion. In this …

Information-Theoretic Determination of Minimax Rates of Convergence Page 1 The Annals
of Statistics 1999, Vol. 27, No. 5, 1564-1599 INFORMATION-THEORETIC …

This book contains the lecture notes for a DMV course presented by the authors at
Gunzburg, Germany, in September, 1990. In the course we sketched the theory of …

We give conditions that guarantee that the posterior probability of every Hellinger
neighborhood of the true distribution tends to 1 almost surely. The conditions are (1) a …