| 书目名称 | Robust Multivariate Analysis | | 编辑 | David J. Olive | | 视频video | | | 概述 | Includes dozens of R functions for making plots and estimators.Problems included at the end of every chapter.Code available for download on the author‘s website.Includes supplementary material: | | 图书封面 |  | | 描述 | This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the multivariate normal distribution, the simpler and more applicable large sample theory is given. The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed by theory. . . The robust techniques are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis. A simple way to bootstrap confidence regions is also provided. . . Much of the research on robust multivariate analysis in this book is being published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics. This graduate text is also useful for people who are familiar with the traditional multivariate topics, but want to know more about handling data sets with outliers. Many R programs and R data sets are available on the author’s website. . | | 出版日期 | Textbook 2017 | | 关键词 | Robust Statistics; Prediction Region; Bootstrap Confidence Region; Multivariate Regression; Principal Co | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-68253-2 | | isbn_softcover | 978-3-319-88571-1 | | isbn_ebook | 978-3-319-68253-2 | | copyright | Springer International Publishing AG 2017 |
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发表于 2025-3-21 19:53:29
