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Smoothness Priors Analysis of Time Series Paperback / softback
by Genshiro Kitagawa, Will Gersch
Part of the Lecture Notes in Statistics series
Paperback / softback
Description
Smoothness Priors Analysis of Time Series addresses some of the problems of modeling stationary and nonstationary time series primarily from a Bayesian stochastic regression "smoothness priors" state space point of view.
Prior distributions on model coefficients are parametrized by hyperparameters.
Maximizing the likelihood of a small number of hyperparameters permits the robust modeling of a time series with relatively complex structure and a very large number of implicitly inferred parameters.
The critical statistical ideas in smoothness priors are the likelihood of the Bayesian model and the use of likelihood as a measure of the goodness of fit of the model.
The emphasis is on a general state space approach in which the recursive conditional distributions for prediction, filtering, and smoothing are realized using a variety of nonstandard methods including numerical integration, a Gaussian 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.
Information
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Item not Available
- Format:Paperback / softback
- Pages:280 pages, X, 280 p.
- Publisher:Springer-Verlag New York Inc.
- Publication Date:09/08/1996
- Category:
- ISBN:9780387948195
Other Formats
- PDF from £93.08
Information
-
Item not Available
- Format:Paperback / softback
- Pages:280 pages, X, 280 p.
- Publisher:Springer-Verlag New York Inc.
- Publication Date:09/08/1996
- Category:
- ISBN:9780387948195