Application of a non-homogeneous Markov chain with seasonal transition probabilities to ozone data
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Taylor & Francis Ltd
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In this work, we assume that the sequence recording whether or not an ozone exceedance of an environmental threshold has occurred in a given day is ruled by a non-homogeneous Markov chain of order one. In order to account for the possible presence of cycles in the empirical transition probabilities, a parametric form incorporating seasonal components is considered. Results show that even though some covariates (namely, relative humidity and temperature) are not included explicitly in the model, their influence is captured in the behavior of the transition probabilities. Parameters are estimated using the Bayesian point of view via Markov chain Monte Carlo algorithms. The model is applied to ozone data obtained from the monitoring network of Mexico City, Mexico. An analysis of how the methodology could be used as an aid in the decision-making is also given.
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Seasonal transition probabilities, Bayesian inference, Markov chain Monte Carlo algorithms, air pollution, Mexico City
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Inglês
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Journal Of Applied Statistics. Abingdon: Taylor & Francis Ltd, v. 46, n. 3, p. 395-415, 2019.
