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 …

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 …

Useful in the theoretical and empirical analysis of nonlinear time series data, semiparametric
methods have received extensive attention in the economics and statistics communities over …

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 …

It was none other than Henri Poincare who at the turn of the last century, recognised that
initial-value sensitivity is a fundamental source of random ness. For statisticians working …

The functional index model consists in assuming that a functional explanatory variable acts
on a scalar response only through its projection on one functional direction. The main aim of …

In this paper we analyze a large class of semiparametric M-estimators for single-index
models, including semiparametric quasi-likelihood and semiparametric maximum likelihood …

We provide a proof for the identifiability for both single-index models and partially linear
single-index models assuming only the continuity of the regression function, a condition …

It has been a long history of using interactions in regression analysis to investigate
alterations in covariate-effects on response variables. In this article, we aim to address two …

Supplement to “Inference for single-index quantile regression models with profile
optimization”. We present several lemmas that will be used in the proof of the main …