Hostname: page-component-7dd5485656-j7khd Total loading time: 0 Render date: 2025-10-31T17:13:23.378Z Has data issue: false hasContentIssue false
  • English
  • Français

Non-negative time series models for dry river flow

Published online by Cambridge University Press:  14 July 2016

Jane L. Hutton
Jane L. Hutton*
Affiliation:
University of Liverpool
*
∗ Postal address: Department of Statistics and Computational Mathematics, University of Liverpool, P.O. Box 147, Liverpool L69 3BX, UK.
Get access Rights & Permissions [Opens in a new window]

Abstract

Non-negative time series which are first-order autoregressive with a mixed exponential innovation process are studied. Properties and approximate marginal distributions for such series are found. Modifications to include exact zeroes and to increase variability so that the time series is a more realistic model of rivers which are dry for part of the year are discussed.

Keywords

EXPONENTIAL AUTOREGRESSIVE MODELSTORAGE MODELSSTREAMFLOWEPHEMERAL STREAMS

Information

Type
Research Papers
Information
Journal of Applied Probability , Volume 27 , Issue 1 , March 1990 , pp. 171 - 182
Copyright
Copyright © Applied Probability Trust 1990 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Abramowitz, M. and Stegun, J. A. (1964) Handbook of Mathematical Functions with Formulae, Graphs and Mathematical Tables. National Bureau of Standards Applied Mathematics Series 55.Google Scholar
Brill, P. H. (1979) An embedded level crossing technique for dams and queues. J. Appl. Prob. 16, 174–186.CrossRefGoogle Scholar
Erdélyi, A. (1954) Tables of Integral Transformations, Vol. 1. McGraw-Hill, New York.Google Scholar
Gaver, D. P. and Lewis, P. A. W. (1980) First order autoregressive gamma sequences and point processes. Adv. Appl. Prob. 12, 727–745.Google Scholar
Hallin, M., Lefevre, C. and Puri, M. L. (1988) On time-reversibility and the uniqueness of moving averages representations for non-Gaussian stationary time series. Biometrika 75, 170–171.CrossRefGoogle Scholar
Hutton, J. L. (1986) Non-negative Time Series and Shot Noise Processes as Models for Dry Rivers. Ph.D. Thesis, University of London.Google Scholar
Lawrance, A. J. and Lewis, P. A. W. (1980) The exponential auto-regressive-moving average EARMA (p, q) process. J. R. Statist. Soc. B 42, 150–161.Google Scholar
Peebles, R. W., Smith, R. E. and Yakowitz, S. J. (1981) A leaky reservoir model for ephemeral streamflow recession. Water Resources Res. 17, 628–636.CrossRefGoogle Scholar
Smith, R. L. (1986) Maximum likelihood estimation for NEAR(2) model. J. R. Statist. Soc. B 48, 251–257.Google Scholar
Weiss, G. (1973) Filtered Poisson Processes as Models for Daily Streamflow Data. Ph.D. Thesis, University of London.Google Scholar