Using non‐homogeneous Poisson models with multiple change‐points to estimate the number of ozone exceedances in Mexico City
Using non‐homogeneous Poisson models with multiple change‐points to estimate the number of ozone exceedances in Mexico City
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使用具有多个变化点的非齐次泊松模型来估计墨西哥城臭氧超标的数量
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
G. Tzintzun
中科院分区：
文献类型：
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作者：
J. Achcar;Eliane R. Rodrigues;G. Tzintzun
在本文中，我们考虑了一些非齐次泊松模型来估计空气质量标准在一个感兴趣的时间间隔内超过给定次数的概率。我们假设根据非齐次泊松过程（NHPP）发生的超标次数。泊松过程具有速率函数$\lambda(t)$, $t \geq 0$，它依赖于一些必须估计的参数。我们考虑了两种情况的速率函数：Weibull和Goel-Okumoto。我们考虑了有和没有变化点的模型。当假设存在变化点时，根据数据集的不同，我们可以有一个、两个或三个变化点。用Gibbs抽样算法估计了速率函数的参数。结果应用于墨西哥城监测网提供的臭氧数据。在第一种情况下，我们假设不存在变化点。根据模型的调整，我们假设存在一个、两个或三个变化点。版权所有©2009 John Wiley ＆ Sons， Ltd
In this paper, we consider some non‐homogeneous Poisson models to estimate the probability that an air quality standard is exceeded a given number of times in a time interval of interest. We assume that the number of exceedances occurs according to a non‐homogeneous Poisson process (NHPP). This Poisson process has rate function $\lambda(t)$, $t \geq 0$, which depends on some parameters that must be estimated. We take into account two cases of rate functions: the Weibull and the Goel—Okumoto. We consider models with and without change‐points. When the presence of change‐points is assumed, we may have the presence of either one, two or three change‐points, depending of the data set. The parameters of the rate functions are estimated using a Gibbs sampling algorithm. Results are applied to ozone data provided by the Mexico City monitoring network. In a first instance, we assume that there are no change‐points present. Depending on the adjustment of the model, we assume the presence of either one, two or three change‐points. Copyright © 2009 John Wiley & Sons, Ltd.
影响因子：
158.5
作者：
Gauderman, WJ;Avol, E;Peters, J
通讯作者：
Peters, J
影响因子：
120.7
作者：
Bell, ML;McDermott, A;Dominici, F
通讯作者：
Dominici, F