Please use this identifier to cite or link to this item:
Title: Statistical modelling of nonlinear long-term cumulative effects
Authors: Kong, E.
Tong, H.
Xia, Y. 
Keywords: Cumulative effect
Generalized additive model
Local linear smoother
Nonlinear time series
Polynomial splines
Single-index model
Issue Date: Jul-2010
Citation: Kong, E.,Tong, H.,Xia, Y. (2010-07). Statistical modelling of nonlinear long-term cumulative effects. Statistica Sinica 20 (3) : 1097-1123. ScholarBank@NUS Repository.
Abstract: In epidemiology, bio-environmental research, and many other scientific areas, the possible long-term cumulative effect of certain factors has been well acknowledged, air pollution on public health, exposure to radiation as a possible cause of cancer, among others. However, there is no known statistical method to model these effects. To fill this gap, we propose a semi-parametric time series model, called the functional additive cumulative time series (FACTS) model, and investigate its statistical properties. We develop an estimation procedure that combines the advantages of kernel smoothing and polynomial spline smoothing. As two case studies, we analyze the effect of air pollutants on respiratory diseases in Hong Kong, and human immunity against influenza in France. Based on the results, some important issues in epidemiology are addressed.
Source Title: Statistica Sinica
ISSN: 10170405
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Jan 20, 2022

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.