| Smoothness Priors Analysis of Time Series 1996 Edition Contributor(s): Kitagawa, Genshiro (Author), Gersch, Will (Author) |
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ISBN: 0387948198 ISBN-13: 9780387948195 Publisher: Springer OUR PRICE: $132.99 Product Type: Paperback - Other Formats Published: August 1996 Annotation: 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. |
| Additional Information |
| BISAC Categories: - Gardening - Mathematics | Probability & Statistics - General - Mathematics | Mathematical Analysis |
| Dewey: 519.55 |
| LCCN: 96022800 |
| Series: Ima Volumes in Mathematics and Its Applications |
| Physical Information: 0.59" H x 6.14" W x 9.21" (0.87 lbs) 280 pages |
| Descriptions, Reviews, Etc. |
| Publisher 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. |