Averaging Time and Maxima for Dependent Observations,
Abstract
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. Extreme value theory can be used if it is reasonable to assume that observations on air pollutant concentrations are independent. Since it is well known that air pollution data is highly correlated, it is reasonable to look upon this data as a time series in which the successive observations are correlated. In the report, the authors present 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. The processes considered are the autoregressive, the Markov and a stationary Gaussian process with a specified autocorrelation function. It is shown that the extreme value distribution provides a lower bound on the distribution function of the maxima of averages of observations generated by an associated stochastic process. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 20, 1972
- Accession Number
- AD0754433
Entities
People
- Nozer Singpurwalla
- Richard E. Barlow
Organizations
- George Washington University