Single-index models are a class of functions given by an unknown univariate``link''function
applied to an unknown one-dimensional projection of the input. These models are …

Searching for an effective dimension reduction space is an important problem in regression,
especially for high dimensional data. We propose an adaptive approach based on …

Single-index models are potentially important tools for multivariate nonparametric
regression. They generalize linear regression by replacing the linear combination α0Tx with …

This paper develops and implements semiparametric methods for estimating binary
response (binary choice) models with continuous endogenous regressors. It extends …

We study the problem of signal estimation from non-linear observations when the signal
belongs to a low-dimensional set buried in a high-dimensional space. A rough heuristic …

This book is intended to introduce graduate students and practicing professionals to some of
the main ideas and methods of semiparametric and nonparametric estimation in …

This book provides an accessible collection of techniques for analyzing nonparametric and
semiparametric regression models. Worked examples include estimation of Engel curves …

One of the most difficult problems in applications of semi-parametric partially linear single-
index models (PLSIM) is the choice of pilot estimators and complexity parameters which may …

The aim of this book is to show that the nonparametric regression can be successfully
applied to system identification and how much can be achieved in this way. It gathers what …

Abstract Generalized Linear Models (GLMs) and Single Index Models (SIMs) provide
powerful generalizations of linear regression, where the target variable is assumed to be a …