Titlebook: Statistics of Random Processes II; Applications R. S. Liptser,A. N. Shiryayev Book 19781st edition Springer Science+Business Media New York

Entrancing

Application of filtering equations to problems of statistics of random sequences,applied to solving various problems of mathematical statistics. In particular, the present section deals with the problem of linear estimation of unobservable components of a multidimensional stationary wide-sense process (discrete time) with rational spectral density in the components accessible for observation.

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Random point processes: Stieltjes stochastic integrals,s, to a certain extent, to those of a Wiener process. Chapters 18 and 19 will deal with the case of an observable process that is a point process whose trajectories are pure jump functions (a Poisson process with constant or variable intensity is a typical example).

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Application of filtering equations to problems of statistics of random sequences,applied to solving various problems of mathematical statistics. In particular, the present section deals with the problem of linear estimation of unobservable components of a multidimensional stationary wide-sense process (discrete time) with rational spectral density in the components accessible fo

Synchronism

Linear estimation of random processes, in finding optimal linear estimates for stationary sequences with rational spectra. Thus it was enough for our purposes to consider the case of Gaussian sequences (Lemma 14.1) for the construction of the optimal mean square linear estimate. This technique will now be used in the problems of linear

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Random point processes: Stieltjes stochastic integrals,s, to a certain extent, to those of a Wiener process. Chapters 18 and 19 will deal with the case of an observable process that is a point process whose trajectories are pure jump functions (a Poisson process with constant or variable intensity is a typical example).

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