Abstract
This chapter focuses on the linear random fields and discusses a class of multidimensional time parameter analogues of the one dimensional prediction problem. It describes autoregressive moving average model (ARMA) processes and ARMA fields. The probability structure of Gaussian processes is determined fully by their first and second order moments. A technique, effective for non-Gaussian linear processes, is in principle effective for non-Gaussian linear random fields. The random variables {vt} of the scheme are independent, identically distributed non-Gaussian variables so that the process {xt} is a non-Gaussian linear random field. As in the one dimensional case, under appropriate conditions, the phase of α(exp[−iλ]) can be completely identified for a non-Gaussian linear random field.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Studies in Econometrics, Time Series, and Multivariate Statistics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
