| 书目名称 | Functional Data Analysis | | 编辑 | J. O. Ramsay,B. W. Silverman | | 视频video | http://file.papertrans.cn/350/349646/349646.mp4 | | 丛书名称 | Springer Series in Statistics | | 图书封面 |  | | 描述 | Scientists today collect samples of curves and other functional observations. This monograph presents many ideas and techniques for such data. Included are expressions in the functional domain of such classics as linear regression, principal components analysis, linear modelling, and canonical correlation analysis, as well as specifically functional techniques such as curve registration and principal differential analysis. Data arising in real applications are used throughout for both motivation and illustration, showing how functional approaches allow us to see new things, especially by exploiting the smoothness of the processes generating the data. The data sets exemplify the wide scope of functional data analysis; they are drawn from growth analysis, meterology, biomechanics, equine science, economics, and medicine. The book presents novel statistical technology while keeping the mathematical level widely accessible. It is designed to appeal to students, to applied data analysts, and to experienced researchers; it will have value both within statistics and across a broad spectrum of other fields. Much of the material is based on the authors‘ own work, some of which appears here | | 出版日期 | Book 19971st edition | | 关键词 | correlation; data analysis; generalized linear model; linear regression; statistics | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4757-7107-7 | | isbn_ebook | 978-1-4757-7107-7Series ISSN 0172-7397 Series E-ISSN 2197-568X | | issn_series | 0172-7397 | | copyright | Springer Science+Business Media New York 1997 |
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