We review different approaches to nonparametric density and regression estimation. Kernel
estimators are motivated from local averaging and solving ill-posed problems. Kernel …

Varying coefficient regression models are known to be very useful tools for analysing the
relation between a response and a group of covariates. Their structure and interpretability …

Spatial transcriptomic studies are becoming increasingly common and large, posing
important statistical and computational challenges for many analytic tasks. Here, we present …

Multiplicative interaction models are widely used in social science to examine whether the
relationship between an outcome and an independent variable changes with a moderating …

Data-analytic approaches to regression problems, arising from many scientific disciplines
are described in this book. The aim of these nonparametric methods is to relax assumptions …

The book describes the use of smoothing techniques in statistics, including both density
estimation and nonparametric regression. Considerable advances in research in this area …

The existence of high speed, inexpensive computing has made it easy to look at data in
ways that were once impossible. Where once a data analyst was forced to make restrictive …

Continuous treatments (eg doses) arise often in practice, but many available causal effect
estimators are limited by either requiring parametric models for the effect curve, or by not …

Amongmanyexcitingdevelopmentsinstatistic…, nonlineartimeseriesanddata-
analyticnonparametricmethodshavegreatly advanced along seemingly unrelated paths. In …

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 …