The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

The statistical and mathematical principles of smoothing with a focus on applicable
techniques are presented in this book. It naturally splits into two parts: The first part is …

This book contains a up-to-date overview of nonlinear time series models and their
application to modelling economic relationships. It considers nonlinear models in stationary …

We introduce the functional generalized additive model (FGAM), a novel regression model
for association studies between a scalar response and a functional predictor. We model the …

We define a new procedure for consistent estimation of nonparametric simultaneous
equations models under the conditional mean independence restriction of Newey et …

Generalized additive models have been popular among statisticians and data analysts in
multivariate nonparametric regression with non-Gaussian responses including binary and …

This paper discusses a nonparametric regression model that naturally generalizes neural
network models. The model is based on a finite number of one-dimensional transformations …

Additive regression for non-Euclidean responses and predictors Page 1 The Annals of
Statistics 2021, Vol. 49, No. 5, 2611–2641 https://doi.org/10.1214/21-AOS2048 © Institute of …

This paper proposes consistent estimators for transformation parameters in semiparametric
models. The problem is to find the optimal transformation into the space of models with a …

In this paper, we propose a semiparametric procedure called the “Model Averaging
MArginal Regression”(MAMAR) that is flexible for forecasting of time series. This procedure …