In this survey paper, we review the constructive algorithms for structure learning in
feedforward neural networks for regression problems. The basic idea is to start with a small …

Uncertainty quantification of predictive models is crucial in decision-making problems.
Conformal prediction is a general and theoretically sound answer. However, it requires …

A new method is presented for flexible regression modeling of high dimensional data. The
model takes the form of an expansion in product spline basis functions, where the number of …

Chemometrics Explore chemometrics from basic statistics to the latest artificial intelligence
and neural network developments in this new edition Chemometrics is an area of study …

We study linear smoothers and their use in building nonparametric regression models. In the
first part of this paper we examine certain aspects of linear smoothers for scatterplots; …

The Halton, Sobol, and Faure sequences and the Braaten-Weller construction of the
generalized Halton sequence are studied in order to assess their applicability for the quasi …

Local regression is a nonparametric method in which the regression surface is estimated by
fitting parametric functions locally in the space of the predictors using weighted least …

Statistical crop modeling is pivotal for understanding climate impacts on crop yields. Choices
of models matter: Linear regression is interpretable but limited in predictive power; machine …

This is a survey article that attempts to synthesize a broad variety of work on splines in
statistics. Splines are presented as a nonparametric function estimating technique. After a …

Statistics and computing share many close relationships. Computing now permeates every
aspect of statistics, from pure description to the development of statistical theory. At the same …