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First-order autoregressive logistic processes

Part of: Stochastic processes Probabilistic methods, simulation and stochastic differential equations Inference from stochastic processes

Published online by Cambridge University Press:  14 July 2016

C. H. Sim
C. H. Sim*
Affiliation:
University of Malaya
*
∗ Postal address: Department of Mathematics, Faculty of Science, University of Malaya, 59 100 Kuala Lumpur, Malaysia.
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Abstract

We propose an AR(1) model that can be used to generate logistic processes. The proposed model has simple probability and correlation structure that can accommodate the full range of attainable correlation. The correlation structure and the joint distribution of the proposed model are given, as well as their conditional mean and variance.

Keywords

BIVARIATE LOGISTIC DISTRIBUTIONSIMULATIONSTOCHASTIC MODEL

MSC classification

Primary: 62M10: Time series, auto-correlation, regression, etc.
Secondary: 65C20: Models, numerical methods 60G10: Stationary processes

Information

Type
Short Communications
Information
Journal of Applied Probability , Volume 30 , Issue 2 , June 1993 , pp. 467 - 470
Copyright
Copyright © Applied Probability Trust 1993 

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References

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Oberhettinger, F. (1973) Fourier Transforms of Distributions and Their Inverses. Academic Press, New York.Google Scholar
Rao, P. S. and Johnson, D. H. (1988) A first-order AR model for non-Gaussian time series. Proc. IEEE Int. Conf. on ASSP. 3, 1534–1537.Google Scholar