Titlebook: Gaussian and Non-Gaussian Linear Time Series and Random Fields; Murray Rosenblatt Book 2000 Springer Science+Business Media New York 2000

antecedence

Homogeneous Gaussian Random Fields,Let ξ(.), . G ., be a random field of real-valued random variables. . is a fixed finite set in . not containing 0. The set of points . ∈ . such that . — . ∈ . is called the .-boundary of the point .. The .-boundary of a set . ⊂ . is the set of points . not in . but in the .-boundary of some point . ∈ ..

Incise

Cumulants, Mixing and Estimation for Gaussian Fields,Later on a number of methods will be introduced that are based on moments of cumulants and are used to estimate aspects of the structure of processes of interest. For this reason it seems proper to make some remarks about moments and cumulants and the relationship between them.

热心助人

arcane

没有准备

Minimum Phase Estimation,uivalent asymptotically in the Gaussian case to maximum likelihood estimates. Consider the stationary ARMA (., .) minimum phase sequence {x.}. with the ξ.’s independent, identically distributed with mean zero and variance σ..

Ibd810

978-1-4612-7067-6Springer Science+Business Media New York 2000

议程

EVEN

https://doi.org/10.1007/978-1-4612-1262-1Covariance matrix; Gaussian Linear Time Series; Likelihood; Linear Time Series; Probability theory; Time

图表证明

Klaus-Geert Heyne,Gabriele Schmiedgenuivalent asymptotically in the Gaussian case to maximum likelihood estimates. Consider the stationary ARMA (., .) minimum phase sequence {x.}. with the ξ.’s independent, identically distributed with mean zero and variance σ..

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