| 书目名称 | Smoothness Priors Analysis of Time Series |
| 编辑 | Genshiro Kitagawa,Will Gersch |
| 视频video | http://file.papertrans.cn/870/869166/869166.mp4 |
| 丛书名称 | Lecture Notes in Statistics |
| 图书封面 |  |
| 描述 | .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. |
| 出版日期 | Book 1996 |
| 关键词 | Likelihood; Smooth function; Time series; Variance; calculus; classification; data analysis; differential e |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4612-0761-0 |
| isbn_softcover | 978-0-387-94819-5 |
| isbn_ebook | 978-1-4612-0761-0Series ISSN 0930-0325 Series E-ISSN 2197-7186 |
| issn_series | 0930-0325 |
| copyright | Springer Science+Business Media New York 1996 |
|Archiver|手机版|小黑屋|
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