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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.
| Autorius: | Will Gersch, Genshiro Kitagawa, |
| Serija: | Lecture Notes in Statistics |
| Leidėjas: | Springer US |
| Išleidimo metai: | 1996 |
| Knygos puslapių skaičius: | 276 |
| ISBN-10: | 0387948198 |
| ISBN-13: | 9780387948195 |
| Formatas: | 235 x 155 x 16 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
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