Stationary exponential time series : further model development and a residual analysis
Lewis, Peter A. W.
Lawrence, A. J.
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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.
RightsThis 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 NumberNPS-55-83-008
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Lewis, Peter A. W.; Lawrance, A. J. (Monterey, California. Naval Postgraduate School, 1984-08); NPS55-84-019An approach to modelling and residual analysis of nonlinear autoregressive time series in exponential variables is presented; the approach is illustrated by analysis of a long series of wind velocity data which has first ...
Lawrance, A.J.; Lewis, P.A.W. (Wiley, 1985);An approach to modelling and residual analysis of nonlinear autoregressive time series in exponential variables is presented; the approach is illustrated by an analysis of a long series of wind velocity data which has first ...
Lewis, Peter A. W. (Monterey, California. Naval Postgraduate School, 1985-06); NPS55-85-009A survey is given of recently developed mathematical models for continuous variate non-Gaussian time series. The emphasis is on marginally specific models with given correlation structure. Exponential, Gamma, Weibull, ...