103 research outputs found
Bootstrap based uncertainty bands for prediction in functional kriging
The increasing interest in spatially correlated functional data has led to
the development of appropriate geostatistical techniques that allow to predict
a curve at an unmonitored location using a functional kriging with external
drift model that takes into account the effect of exogenous variables (either
scalar or functional). Nevertheless uncertainty evaluation for functional
spatial prediction remains an open issue. We propose a semi-parametric
bootstrap for spatially correlated functional data that allows to evaluate the
uncertainty of a predicted curve, ensuring that the spatial dependence
structure is maintained in the bootstrap samples. The performance of the
proposed methodology is assessed via a simulation study. Moreover, the approach
is illustrated on a well known data set of Canadian temperature and on a real
data set of PM concentration in the Piemonte region, Italy. Based on the
results it can be concluded that the method is computationally feasible and
suitable for quantifying the uncertainty around a predicted curve.
Supplementary material including R code is available upon request
Modeling threshold exceedance probabilities of spatially correlated time series
The Commission of the European Union, as well the United States Environmental
Protection Agency, have set limit values for some pollutants in the ambient air
that have been shown to have adverse effects on human and environmental health.
It is therefore important to identify regions where the probability of
exceeding those limits is high. We propose a two-step procedure for estimating
the probability of exceeding the legal limits that combines smoothing in the
time domain with spatial interpolation. For illustration, we show an
application to particulate matter with diameter less than 10 microns
(PM) in the North-Italian region Piemonte.Comment: Published in at http://dx.doi.org/10.1214/08-EJS252 the Electronic
Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Modeling the Short-Term Effect of Traffic and Meteorology on Air Pollution in Turin with Generalized Additive Models
Vehicular traffic plays an important role in atmospheric pollution and can be used as one of the key predictors in air-quality forecasting models. The models that can account for the role of traffic are especially valuable in urban areas, where high pollutant concentrations are often observed during particular times of day (rush hour) and year (winter). In this paper, we develop a generalized additive models approach to analyze the behavior of concentrations of nitrogen dioxide (NO2), and particulate matter (PM10), collected at the environmental monitoring stations distributed throughout the city of Turin, Italy, from December 2003 to April 2005. We describe nonlinear relationships between predictors and pollutants, that are adjusted for unobserved time-varying confounders. We examine several functional forms for the traffic variable and find that a simple form can often provide adequate modeling power. Our analysis shows that there is a saturation effect of traffic on NO2, while such saturation is less evident in models linking traffic to PM10behavior, having adjusted for meteorological covariates. Moreover, we consider the proposed models separately by seasons and highlight similarities and differences in the predictors' partial effects. Finally, we show how forecasting can help in evaluating traffic regulation policies
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