Dimension Reduction Transfer Function Model

• Activity Number: 39
• Type: Contributed
• Date/Time: Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
• Sponsor: IMS
• Author(s): Jin-Hong Park*+
• Companies: College of Charleston
• Keywords: Dimension reduction ; Central mean subspace in time series ; Transfer function model ; Nadaraya-Watson kernel smoother ; Nonliner time series

Abstract

The dimension reduction in regression is an efficient method of overcoming the curse of dimensionality in nonparametric regression. Motivated by recent developments for dimension reduction in time series, an empirical extension of central mean subspace in time series to a single-input transfer function model is performed in this paper. Here, we use central mean subspace as a tool of dimension reduction for bivariate time series in case when the dimension and lag are known, and estimate the central mean subspace through the Nadaraya-Watson kernel smoother. Furthermore, we develop a data-dependent approach based on a modified Schwarz Bayesian criterion to estimate unknown dimension and lag. Finally, we show that the approach in bivariate time series works well using an expository demonstration, two simulations, and a real data analysis such as El Nino and Fish Population.