Marginally Specific Alternatives to Normal Arma Processes
Lewis, Peter A.W.
DeWald, Lee S. Sr.
MetadataShow full item record
In many practical cases in time series analysis, marginal distributions in stationary situations are not Gaussian. It is therefore necessary to be able to generate and analyze nonGaussian time series. Several non-Gaussian time series models are discussed in this paper. The marginal distributions are Laplace or I-Laplace distributions, and the correlation structure of the processes mimics that of the standard additive, linear, constant coefficient ARMA(p,q) models.
Proceedings of the 1987 Winter Simulation Conference, A. Thesen, H. Grant, W. David Kelton (eds.)
Showing items related by title, author, creator and subject.
Hauke, Matthew D. (Monterey, California. Naval Postgraduate School, 2006-06);A new Tropical Cyclone (TC) surface wind speed probability product from the National Hurricane Center (NHC) takes into account uncertainty in track, maximum wind speed, and wind radii. A Monte Carlo (MC) model is used ...
The distribution of wave heights and periods for seas with unimodal and bimodal power density spectra Sharpe, Matthew Michael (1990);Observed distributions of wave heights and periods taken from one year of surface wave monitoring near Martha's Vineyard are compared to distributions based on narrow-band theory. The joint distributions of wave heights ...
Parker, Robert Earl, Jr. (Monterey, California. Naval Postgraduate School, 1992-09);Estimating the spectra of non-stationary signals represents a difficult challenge. Classical techniques employing the Fourier transform and local stationarity have been employed with limited success. A more promising ...