Amongmanyexcitingdevelopmentsinstatistic…, nonlineartimeseriesanddata-
analyticnonparametricmethodshavegreatly advanced along seemingly unrelated paths. In …

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 …

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 …

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 …

Varying-coefficient linear models arise from multivariate nonparametric regression, non-
linear time series modelling and forecasting, functional data analysis, longitudinal data …

In this article, we study the contextual dynamic pricing problem where the market value of a
product is linear in its observed features plus some market noise. Products are sold one at a …

For the past two decades, the single-index model, a special case of projection pursuit
regression, has proven to be an efficient way of coping with the high-dimensional problem in …

A flexible nonparametric regression model is considered in which the response depends
linearly on some covariates, with regression coefficients as additive functions of other …

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

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 …