Averaging Time and Maxima for Dependent Observations.
CALIFORNIA UNIV BERKELEY OPERATIONS RESEARCH CENTER
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For the purposes of evaluating air quality, it is important to know the probability that maximum pollutant concentrations will exceed state standards stated for various averaging times. In the paper, the authors a new approach for analyzing certain time series processes. They show that, under certain conditions, several stochastic processes which could generate a time series for air pollutant data are associated. Association is a strengthening of the concept of positive correlation. The processes that are considered here are the autoregressive, the Markov, and a stationary Gaussian process with a specified autocorrelation function. The extreme value distribution is shown to provide a lower bound on the distribution function of the maxima of averages of observations generated by an associated stochastic process.
- Statistics and Probability
- Air Pollution and Control