scholarly journals Numerical Simulation of Non‐Gaussian Random Fields with Prescribed Correlation Structure

2001 ◽  
Vol 113 (786) ◽  
pp. 1009-1020 ◽  
Author(s):  
Roberto Vio ◽  
Paola Andreani ◽  
Willem Wamsteker
Keyword(s):  
Numerical Simulation ◽  
Random Fields ◽  
Gaussian Random Fields ◽  
Correlation Structure ◽  
Non Gaussian

scholarly journals Random Fields with Pólya Correlation Structure

2014 ◽  
Vol 51 (4) ◽  
pp. 1037-1050 ◽  
Author(s):  
Richard Finlay ◽  
Eugene Seneta
Keyword(s):  
Random Fields ◽  
Cross Correlation ◽  
Marginal Distribution ◽  
Gaussian Random Fields ◽  
Correlation Structure ◽  
Finite Variance ◽  
Infinitely Divisible ◽  
New Class ◽  
Joint Characteristic ◽  
Non Gaussian

We construct random fields with Pólya-type autocorrelation function and dampened Pólya cross-correlation function. The marginal distribution of the random fields may be taken as any infinitely divisible distribution with finite variance, and the random fields are fully characterized in terms of their joint characteristic function. This makes available a new class of non-Gaussian random fields with flexible correlation structure for use in modeling and estimation.

Random Fields with Pólya Correlation Structure

2014 ◽  
Vol 51 (04) ◽  
pp. 1037-1050 ◽  
Author(s):  
Richard Finlay ◽  
Eugene Seneta
Keyword(s):  
Random Fields ◽  
Cross Correlation ◽  
Marginal Distribution ◽  
Gaussian Random Fields ◽  
Correlation Structure ◽  
Finite Variance ◽  
Infinitely Divisible ◽  
New Class ◽  
Joint Characteristic ◽  
Non Gaussian

We construct random fields with Pólya-type autocorrelation function and dampened Pólya cross-correlation function. The marginal distribution of the random fields may be taken as any infinitely divisible distribution with finite variance, and the random fields are fully characterized in terms of their joint characteristic function. This makes available a new class of non-Gaussian random fields with flexible correlation structure for use in modeling and estimation.

scholarly journals Inference on dynamic models for non-Gaussian random fields using INLA

2017 ◽  
Vol 31 (1) ◽  
pp. 1-23 ◽  
Author(s):  
R. X. Cortes ◽  
T. G. Martins ◽  
M. O. Prates ◽  
B. A. Silva
Keyword(s):  
Random Fields ◽  
Dynamic Models ◽  
Gaussian Random Fields ◽  
Non Gaussian
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