In the past 10years a statistical technique, approximate Bayesian computation (ABC), has
been developed that can be used to infer parameters and choose between models in the …

This paper surveys locally weighted learning, a form of lazy learning and memory-based
learning, and focuses on locally weighted linear regression. The survey discusses distance …

Ab initio molecular dynamics (AIMD) simulation is widely employed in studying diffusion
mechanisms and in quantifying diffusional properties of materials. However, AIMD …

Nonparametric methods play a central role in modern empirical work. While they provide
inference procedures that are more robust to parametric misspecification bias, they may be …

Sufficient dimension reduction is a rapidly developing research field that has wide
applications in regression diagnostics, data visualization, machine learning, genomics …

Long-memory, or more generally fractal, processes are known to play an important role in
many scientific disciplines and applied fields such as physics, geophysics, hydrology …

We consider nonparametric identification and estimation in a nonseparable model where a
continuous regressor of interest is a known, deterministic, but kinked function of an observed …

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 trade-off between the temporal and spatial resolutions, and/or the influence of cloud
cover, makes it difficult to obtain continuous fine-scale satellite data for surface urban heat …

We investigate the choice of the bandwidth for the regression discontinuity estimator. We
focus on estimation by local linear regression, which was shown to have attractive properties …