Stationary exponential time series : further model development and a residual analysis
Abstract
A second order autoregressive process in exponential variables, NEAR(2),
is established: the distributional assumptions involved in this model highlight
a yery broad four parameter structure which combines five exponential
random variables into a sixth exponential random variable. The dependency
structure of the NEAR(2) process beyond and including autocorrelations is
explored using some new ideas on residual analysis for non-normal processes
with autoregressive correlation structure. Other applications of the exponential
structure are considered briefly. These include exponential time
series with negative correlation and exponential time series with mixed
autoregressive-moving average structure. An application to the analysis of
a set of wind speed data is included.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.NPS Report Number
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