Smoothness Priors Analysis of Time Series /
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 hyperpa...
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY :
Springer New York : Imprint : Springer,
1996.
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| Series: | Lecture notes in statistics (Springer-Verlag) ;
116. |
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| Online Access: | Connect to the full text of this electronic book |
| Summary: | 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. |
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| Item Description: | Electronic resource. |
| Physical Description: | 1 online resource (x, 280 pages 40 illustrations) |
| ISBN: | 9781461207610 (electronic bk.) 1461207614 (electronic bk.) |