Statistical Modelling of Nonlinear Long-Term Cumulative Effects

Kong, Efang and Tong, Howell and Xia, Yingcun (2010) Statistical Modelling of Nonlinear Long-Term Cumulative Effects. Statistica Sinica, 20 (3). pp. 1097-1123. ISSN 1017-0405. (Full text available)

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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.

Item Type: Article
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Efang Kong
Date Deposited: 29 Jun 2011 13:38
Last Modified: 28 May 2014 10:26
Resource URI: http://kar.kent.ac.uk/id/eprint/23953 (The current URI for this page, for reference purposes)
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