| 书目名称 | Robust and Nonlinear Time Series Analysis | | 副标题 | Proceedings of a Wor | | 编辑 | Jürgen Franke,Wolfgang Härdle,Douglas Martin | | 视频video | | | 丛书名称 | Lecture Notes in Statistics | | 图书封面 |  | | 描述 | Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. The, most commonly used assumption is that the data is a realization of a stationary Gaussian process. However, since the Gaussian assumption is a fairly stringent one, this assumption is frequently replaced by the weaker assumption that the process is wide~sense stationary and that only the mean and covariance sequence is specified. This approach of specifying the probabilistic behavior only up to "second order" has of course been extremely popular from a theoretical point of view be cause it has allowed one to treat a large variety of problems, such as prediction, filtering and smoothing, using the geometry of Hilbert spaces. While the literature abounds with a variety of optimal estimation results based on either the Gaussian assumption or the specification of second-order properties, time series workers have not always believed in the literal truth of either the Gaussian or second-order specifica tion. They have none-the-less stressed the importance of such optimali ty results, probably for two main reasons: First, the results come from a rich and | | 出版日期 | Conference proceedings 1984 | | 关键词 | Analysis; Estimator; Series; Time; Time series; Variance; correlation | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4615-7821-5 | | isbn_softcover | 978-0-387-96102-6 | | isbn_ebook | 978-1-4615-7821-5Series ISSN 0930-0325 Series E-ISSN 2197-7186 | | issn_series | 0930-0325 | | copyright | Springer-Verlag Berlin Heidelberg 1984 |
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发表于 2025-3-21 18:40:50
