Nonparametric regression techniques are often sensitive to the presence of correlation in
the errors. The practical consequences of this sensitivity are explained, including the …

We suggest quantile regression methods for a class of smooth coefficient time series
models. We use both local polynomial and local constant fitting schemes to estimate the …

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 …

When additive models with more than two covariates are fitted with the backfitting algorithm
proposed by Buja et al.[2], the lack of explicit expressions for the estimators makes study of …

This study estimates a hedonic price function using a semi-parametric regression and
compares the price prediction performance with conventional parametric models. This study …

We explore additive models that combine both parametric and nonparametric terms and
propose a√ n-consistent backfitting estimator for the parametric component of the model …

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

The advance of technology facilitates the collection of statistical data. Flexible and refined
statistical models are widely sought in a large array of statistical problems. The question …

We model a hedonic price function for housing as an additive nonparametric regression.
Estimation is done via a backfitting procedure in combination with a local polynomial …

This paper aims to present an overview of Semiparametric additive models. An estimation of
the finite‐parameters of semiparametric regression models that involve additive …