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

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書目名稱Gaussian and Non-Gaussian Linear Time Series and Random Fields影響因子(影響力)




書目名稱Gaussian and Non-Gaussian Linear Time Series and Random Fields影響因子(影響力)學(xué)科排名




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書目名稱Gaussian and Non-Gaussian Linear Time Series and Random Fields網(wǎng)絡(luò)公開度學(xué)科排名




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書目名稱Gaussian and Non-Gaussian Linear Time Series and Random Fields被引頻次學(xué)科排名




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Gaussian and Non-Gaussian Linear Time Series and Random Fields

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Book 2000e increasing in the usual manner is the same as that with time reversed. Chapter 1 considers the question of reversibility for linear stationary sequences and gives necessary and sufficient conditions for the reversibility. A neat result of Breidt and Davis on reversibility is presented. A sim- ple

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Bedingte Reflexe, die Lernmatrix,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.

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https://doi.org/10.1007/978-3-662-00604-7Assume that . is a stationary ARMA scheine satisfying the system of equations. where the ξ.’s are independent and identically distributed with .ξ. = 0 and .ξ. = σ. > 0. Consider the prediction problem in which one approximates x. by a function of x., s ≤ 0, in mean square as well as one can.

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Reversibility and Identifiability,Let us first consider linear stationary sequences. A sequence of independent, identically distributed real random variables ξ., j = …, -1,0,1,… is given with Eξ. = 0, 0 < .ξ. = σ. < ∞. The process x. is obtained by passing this sequence through a linear filter characterized by the real weights, ., ∑. < ∞,