Accession Number : AD0754433

Title :   Averaging Time and Maxima for Dependent Observations,

Corporate Author : GEORGE WASHINGTON UNIV WASHINGTON D C PROGRAM IN LOGISTICS

Personal Author(s) : Singpurwalla,Nozer D. ; Barlow,Richard E.

Report Date : 20 DEC 1972

Pagination or Media Count : 34

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)

Descriptors :   (*AIR POLLUTION, TIME SERIES ANALYSIS), STANDARDS, STATISTICAL FUNCTIONS, CONCENTRATION(CHEMISTRY), STOCHASTIC PROCESSES, CORRELATION TECHNIQUES

Subject Categories : Air Pollution and Control

Distribution Statement : APPROVED FOR PUBLIC RELEASE