Titlebook: Smoothness Priors Analysis of Time Series; Genshiro Kitagawa,Will Gersch Book 1996 Springer Science+Business Media New York 1996 Likelihoo

粗鲁的人

anatomical

朝圣者

制定

Seasonal Adjustment,me series by the more general non-Gaussian state space modeling methods. In the more general non-Gaussian state space trend plus seasonal modeling, state estimation is achieved here in two different way, using the Gaussian sum and Monte Carlo filter methods.

记成蚂蚁

Modeling Scalar Nonstationary Covariance Time Series,ta. That topic is treated in Chapter 12.) Our primary application for the scalar nonstationary covariance modeling is the evolution with time of the power spectrum. The estimated TVAR model yields what we refer to as an “instantaneous power spectral density”.

Inordinate

Modeling Multivariate Nonstationary Covariance Time Series,radigm. This idea is a consequence of an instantaneous response-orthogonal innovations representation of multivariate AR models. That permits the multivariate time series to be modeled one scalar AR model at-a-time.

事物的方面

Quasi-Periodic Process Modeling,ynx data, (for example see Campbell and Walker 1977, Tong 1977, Bhansali 1979 and Priestly 1981), and the Wolfer sunspot series, (Morris 1977, Tong 1983). Such series are frequently modeled by AR, ARMA or AR plus sinusoidal models. However, none of these modeling methods are very satisfactory for th

广大

Book 1996aussian mixture distribution-two filter smoothing formula, and a Monte Carlo "particle-path tracing" method in which the distributions are approximated by many realizations. The methods are applicable for modeling time series with complex structures.

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