University of A Coruña, Faculty of Computer Science, Campus de Eviña, s/n, A Coruña 15071, Spain.
Chemosphere. 2011 Feb;82(6):800-8. doi: 10.1016/j.chemosphere.2010.11.025. Epub 2010 Dec 7.
The study of extreme values and prediction of ozone data is an important topic of research when dealing with environmental problems. Classical extreme value theory is usually used in air-pollution studies. It consists in fitting a parametric generalised extreme value (GEV) distribution to a data set of extreme values, and using the estimated distribution to compute return levels and other quantities of interest. Here, we propose to estimate these values using nonparametric functional data methods. Functional data analysis is a relatively new statistical methodology that generally deals with data consisting of curves or multi-dimensional variables. In this paper, we use this technique, jointly with nonparametric curve estimation, to provide alternatives to the usual parametric statistical tools. The nonparametric estimators are applied to real samples of maximum ozone values obtained from several monitoring stations belonging to the Automatic Urban and Rural Network (AURN) in the UK. The results show that nonparametric estimators work satisfactorily, outperforming the behaviour of classical parametric estimators. Functional data analysis is also used to predict stratospheric ozone concentrations. We show an application, using the data set of mean monthly ozone concentrations in Arosa, Switzerland, and the results are compared with those obtained by classical time series (ARIMA) analysis.
研究极端值和预测臭氧数据是处理环境问题时的一个重要研究课题。经典的极值理论通常用于空气污染研究。它包括将参数广义极值（GEV）分布拟合到极端值数据集，并使用估计的分布计算回返水平和其他感兴趣的数量。在这里，我们建议使用非参数函数数据方法来估计这些值。功能数据分析是一种相对较新的统计方法，通常用于处理由曲线或多维变量组成的数据。在本文中，我们使用这种技术，结合非参数曲线估计，为常用的参数统计工具提供替代方案。非参数估计器应用于从英国自动城市和农村网络（AURN）的几个监测站获得的最大臭氧值的实际样本。结果表明，非参数估计器的性能令人满意，优于经典参数估计器的性能。功能数据分析也用于预测平流层臭氧浓度。我们展示了一个应用，使用瑞士阿罗萨的月平均臭氧浓度数据集，并将结果与经典时间序列（ARIMA）分析的结果进行比较。
