Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-28T22:10:48.486Z Has data issue: false hasContentIssue false

Minification processes and their transformations

Published online by Cambridge University Press:  14 July 2016

Peter A. W. Lewis*
Affiliation:
Naval Postgraduate School
Ed McKenzie*
Affiliation:
University of Strathclyde
*
Postal address: Department of Operations Research, Naval Postgraduate School, Monterey, CA 93940, USA.
∗∗Postal address: Department of Statistics, University of Strathclyde, Glasgow, UK.

Abstract

It is shown that the stationary, autoregressive, Markovian minification processes introduced by Tavares and Sim can be extended to give processes with marginal distributions other than the exponential and Weibull distributions. Necessary and sufficient conditions on the hazard rate of the marginal distributions are given for a minification process to exist. Results are given for the derivation of the autocorrelation function; these correct the expression for the Weibull given by Sim. Monotonic transformations of the minification processes are also discussed and generate a whole new class of autoregressive processes with fixed marginal distributions. Stationary processes generated by a maximum operation are also introduced and a comparison of three different Markovian processes with uniform marginal distributions is given.

Type
Research Papers
Copyright
Copyright © Applied Probability Trust 1991 

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.)

References

Brown, B. G., Katz, R. W. and Murphy, A. H. (1984) Time series models to simulate and forecast wind speed and wind power. J. Climate Appl. Meteorol. 23, 11841195.Google Scholar
Chernick, M. R., Daley, D. J. and Littlejohn, R. P. (1988) A time-reversibility relationship between two Markov chains with exponential stationary distributions. J. Appl. Prob. 25, 418422.Google Scholar
Gaver, D. P. and Acar, M. (1979) Analytical hazard representations for use in reliability, mortality, and simulation studies. Commun. Statist. – Simul. Comput. B12, 91111.Google Scholar
Gaver, D. P. and Lewis, P. A. W. (1980) First-order autogressive gamma sequences and point processes. Adv. Appl. Prob. 12, 727745.CrossRefGoogle Scholar
Johnson, N. L. and Kotz, S. (1970) Distributions in Statistics – Continuous Univariate Distributions – I. Wiley, New York.Google Scholar
Lewis, P. A. W. (1985) Some simple models for continuous variate time series. Water Resources Bull. 21, 635644.Google Scholar
Sim, C. H. (1986) Simulation of Weibull and Gamma autoregressive stationary process. Commun. Statis. – Simul. Comput. B15, 11411146.Google Scholar
Tavares, L. V. (1977) The exact distribution of extremes of a non-Gaussian process. Stoch. Proc. Appl. 5, 151156.Google Scholar
Tavares, L. V. (1980a) An exponential Markovian stationary process. J. Appl. Prob. 17, 11171120.CrossRefGoogle Scholar
Tavares, L. V. (1980b) A non-Gaussian Markovian model to simulate hydrologic processes. J. Appl. Prob. 46, 281287.Google Scholar