On the one hand, kernel density estimation has become a common tool for empirical studies
in any research area. This goes hand in hand with the fact that this kind of estimator is now …

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 …

A comprehensive and cutting-edge introduction to the foundations and modern applications
of learning theory. Research has exploded in the field of machine learning resulting in …

Orthogonal series density estimation is a powerful nonparametric estimation methodology
that allows one to analyze and present data at hand without any prior opinion about shape …

We address the problem of density estimation with" L_s-loss by selection of kernel
estimators. We develop a selection procedure and derive corresponding L_s-risk oracle …

Accurate and reliable predictions of infectious disease dynamics can be valuable to public
health organizations that plan interventions to decrease or prevent disease transmission. A …

Causal effects are often characterized with averages, which can give an incomplete picture
of the underlying counterfactual distributions. Here we consider estimating the entire …

This paper studies V-fold cross-validation for model selection in least-squares density
estimation. The goal is to provide theoretical grounds for choosing V in order to minimize the …

This paper describes a novel approach to change-point detection when the observed high-
dimensional data may have missing elements. The performance of classical methods for …

We address the problem of adaptive minimax density estimation on R^ d R d with L _p L p-
loss on the anisotropic Nikol'skii classes. We fully characterize behavior of the minimax risk …