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A class of stationary random fields with a simple correlation structure

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  • Ma, Chunsheng

Abstract

A stationary random field is often more complicated than a univariate stationary time series, since dependence for a random field extends in all directions, while there is only the natural distinction of past and future at any instant in a univariate time series. In this paper we start from a simple correlation structure, derive a class of stationary random fields with the simple correlation function and the simple spectral density function by using linear combinations of separable spatial correlation functions, and discuss a problem of embedding a lattice model into a continuous domain model.

Suggested Citation

  • Ma, Chunsheng, 2005. "A class of stationary random fields with a simple correlation structure," Journal of Multivariate Analysis, Elsevier, vol. 94(2), pages 313-327, June.
    • Handle: RePEc:eee:jmvana:v:94:y:2005:i:2:p:313-327
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    References listed on IDEAS

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    1. Ma, Chunsheng, 2004. "Spatial autoregression and related spatio-temporal models," Journal of Multivariate Analysis, Elsevier, vol. 88(1), pages 152-162, January.
    2. Hansen, Lars Peter & Sargent, Thomas J, 1983. "The Dimensionality of the Aliasing Problem in Models with Rational Spectral Densities," Econometrica, Econometric Society, vol. 51(2), pages 377-387, March.
    3. K. S. Chan & H. Tong, 1987. "A Note On Embedding A Discrete Parameter Arma Model In A Continuous Parameter Arma Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 8(3), pages 277-281, May.
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