The focus of this paper is using nonparametric transfer function models in forecasting. Nonparametric smoothing methods are used to model the relationship between variables (the transfer function) and the noise is modeled as an Autoregressive Moving Average (ARMA) process. The transfer function is estimated jointly with the ARMA parameters. Nonparametric smoothing methods are flexible thus can ...

In traditional parametric regression models, the functional form of the model is specified before the model is fit to data, and the object is to estimate the parameters of the model. In nonparametric regression, in contrast, the object is to estimate the regression function directly without specifying its form explicitly. In this appendix to Fox and Weisberg (2011), we describe how to fit sever...

A relatively recent development in nonparametric regression is the representation of spline-based smoothers as mixed model fits. In particular, generalized nonparametric regression (e.g. smoothingwith a binary response) corresponds to fitting a generalized linear mixedmodel. Automation, or data-driven smoothing parameter selection, can be achieved via (restricted) maximum likelihood estimation ...