Timescale effect estimation in time-series studies of air pollution and health: A Singular Spectrum Analysis approach

A wealth of epidemiological data suggests an association between mortality/morbidity from pulmonary and cardiovascular adverse events and air pollution, but uncertainty remains as to the extent implied by those associations although the abundance of the data. In this paper we describe an SSA (Singular Spectrum Analysis) based approach in order to decompose the time-series of particulate matter concentration into a set of exposure variables, each one representing a different timescale. We implement our methodology to investigate both acute and long-term effects of $PM_{10}$ exposure on morbidity from respiratory causes within the urban area of Bari, Italy.Comment: Published in at http://dx.doi.org/10.1214/07-EJS123 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

Singular Spectrum Analysis: a new decomposition technique applied to environmental systems

EnIn the last few years Singular Spectrum Analysis (SSA), a powerful tool in time series In the last few years Singular Spectrum Analysis (SSA), a powerful tool in time series reconstruction of components may based on the functional clustering algorithm introduced in Bilancia and Stea (2008). We report an example concerning an application in the environmental health field

Singular Spectrum Analysis: a new decomposition technique applied to environmental systems

EnIn the last few years Singular Spectrum Analysis (SSA), a powerful tool in time series In the last few years Singular Spectrum Analysis (SSA), a powerful tool in time series reconstruction of components may based on the functional clustering algorithm introduced in Bilancia and Stea (2008). We report an example concerning an application in the environmental health field