Abstract:A Deus, primeiramente, que sempre esteve ao meu lado. A minha namorada Jáina, por ser tão especial e hoje, ser a pessoa mais importante em minha vida. Ao meu orientador, Prof. Dr. Jorge Alberto Achcar, pelaótima e competente orientação, pela paciência e por ter acreditado em mim quando poucos o teriam feito. Ao Prof. Dr. Edson Zangiacomi Martinez, por todos os momentos de ajuda e companheirismo prestados, pelaótima orientação que recebi em meu estágio e principalmente, pela oportunidade oferecida no Centro de … Show more
“…Also from Table 1, we may see that the effect of the parameter on the latent variables is a negative one in all regions. That effect is also observed in similar studies ( [22,23]). Note that in all regions the value of is very small.…”
Section: Discussionsupporting
confidence: 86%
“…That means that the effect of past values in the present ones is very small. That same behaviour is also seen in [22,23]. However, for the present dataset the effect is even smaller.…”
Section: Discussionsupporting
confidence: 86%
“…A decreasing of the daily concentration might occur, but it is also interesting to know if the variability also decreases. Following in that direction we have, for instance, [21,22] and [23] where some univariate and bivariate stochastic volatility models are used to study the weekly averaged ozone concentration in Mexico City. Also, in [23] and [24] we have the use of multivariate stochastic volatility models to study the behaviour of five pollutants present in the city of São Paulo, Brazil.…”
Section: General Literature Reviewmentioning
confidence: 99%
“…The model considered here is a particular case of the multivariate case considered in [23] and [24]. The difference here is that instead of using five pollutants we are going to concentrate only on ozone measurements obtained in five regions of Mexico City.…”
Section: Description Of the Problem In Mexico Citymentioning
confidence: 99%
“…The choice of prior distributions for the parameters was based on information obtained from previous studies ( [22][23][24]) and also from the behaviour presented by the data.…”
Section: Note That the Likelihood Functionmentioning
<span>In this work we consider a stochastic volatility model, commonly used in financial time series studies, to analyse ozone data. The model considered depends on some parameters and in order to estimate them a Markov chain Monte Carlo algorithm is proposed. The algorithm considered here is the so-called Gibbs sampling algorithm which is programmed using the language R. Its code is also given. The model and the algorithm are applied to the weekly ozone averaged measurements obtained from the monitoring network of Mexico City.</span> <p> <span></span> </p>
“…Also from Table 1, we may see that the effect of the parameter on the latent variables is a negative one in all regions. That effect is also observed in similar studies ( [22,23]). Note that in all regions the value of is very small.…”
Section: Discussionsupporting
confidence: 86%
“…That means that the effect of past values in the present ones is very small. That same behaviour is also seen in [22,23]. However, for the present dataset the effect is even smaller.…”
Section: Discussionsupporting
confidence: 86%
“…A decreasing of the daily concentration might occur, but it is also interesting to know if the variability also decreases. Following in that direction we have, for instance, [21,22] and [23] where some univariate and bivariate stochastic volatility models are used to study the weekly averaged ozone concentration in Mexico City. Also, in [23] and [24] we have the use of multivariate stochastic volatility models to study the behaviour of five pollutants present in the city of São Paulo, Brazil.…”
Section: General Literature Reviewmentioning
confidence: 99%
“…The model considered here is a particular case of the multivariate case considered in [23] and [24]. The difference here is that instead of using five pollutants we are going to concentrate only on ozone measurements obtained in five regions of Mexico City.…”
Section: Description Of the Problem In Mexico Citymentioning
confidence: 99%
“…The choice of prior distributions for the parameters was based on information obtained from previous studies ( [22][23][24]) and also from the behaviour presented by the data.…”
Section: Note That the Likelihood Functionmentioning
<span>In this work we consider a stochastic volatility model, commonly used in financial time series studies, to analyse ozone data. The model considered depends on some parameters and in order to estimate them a Markov chain Monte Carlo algorithm is proposed. The algorithm considered here is the so-called Gibbs sampling algorithm which is programmed using the language R. Its code is also given. The model and the algorithm are applied to the weekly ozone averaged measurements obtained from the monitoring network of Mexico City.</span> <p> <span></span> </p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.