| 书目名称 | Dynamic Data Analysis |
| 副标题 | Modeling Data with D |
| 编辑 | James Ramsay,Giles Hooker |
| 视频video | |
| 概述 | Offers an accessible text to those with little or no exposure to differential equations as modeling objects.Updates and builds on techniques from the popular Functional Data Analysis (Ramsay and Silve |
| 丛书名称 | Springer Series in Statistics |
| 图书封面 |  |
| 描述 | This text focuses on the use of smoothing methods for developing and estimating differential equations following recent developments in functional data analysis and building on techniques described in Ramsay and Silverman (2005) .Functional Data Analysis.. The central concept of a dynamical system as a buffer that translates sudden changes in input into smooth controlled output responses has led to applications of previously analyzed data, opening up entirely new opportunities for dynamical systems. The technical level has been kept low so that those with little or no exposure to differential equations as modeling objects can be brought into this data analysis landscape. There are already many texts on the mathematical properties of ordinary differential equations, or dynamic models, and there is a large literature distributed over many fields on models for real world processes consisting of differential equations. However, a researcher interested in fitting such a model to data, or a statistician interested in the properties of differential equations estimated from data will find rather less to work with. This book fills that gap. . |
| 出版日期 | Book 2017 |
| 关键词 | functional data analysis; differential equations; dynamic models; linear differential equations; nonline |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4939-7190-9 |
| isbn_softcover | 978-1-4939-8412-1 |
| isbn_ebook | 978-1-4939-7190-9Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer Science+Business Media LLC 2017 |
发表于 2025-3-21 19:52:19
