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Titlebook: Applied Multivariate Statistical Analysis; Wolfgang Karl Härdle,Léopold Simar Textbook Jan 20123rd edition Springer-Verlag GmbH Germany, p

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期刊全称Applied Multivariate Statistical Analysis
影响因子2023Wolfgang Karl Härdle,Léopold Simar
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发行地址Revised and updated third edition offers a broader range of material Wide scope of methods and applications, making this a comprehensive treatment of the subject A wealth of examples and exercises – i
图书封面Titlebook: Applied Multivariate Statistical Analysis;  Wolfgang Karl Härdle,Léopold Simar Textbook Jan 20123rd edition Springer-Verlag GmbH Germany, p
影响因子.Most of the observable phenomena in the empirical sciences are of a multivariate nature.  In financial studies, assets are observed simultaneously and their joint development is analysed to better understand general risk and to track indices.  In medicine recorded observations of subjects in different locations are the basis of reliable diagnoses and medication.  In quantitative marketing consumer preferences are collected in order to construct models of consumer behavior.  The underlying data structure of these and many other quantitative studies of applied sciences is multivariate.  Focusing on applications this book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who need to analyze statistical data.  The book surveys the basic principles of multivariate statistical data analysis and emphasizes both exploratory and inferential statistics.  All chapters have exercises that highlight applications in different fields..The third edition of this book on Applied Multivariate Statistical Analysis offers the following new features.A new Chapter on Regression Models has been added.All numerical exampl
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Multivariate Distributionsation on the relationship between the variables can be made available. Only basic statistical theory was used to derive tests of independence or of linear relationships. In this chapter we give an introduction to the basic probability tools useful in statistical multivariate analysis.
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Theory of the Multinormalistribution, since it is often a good approximate distribution in many situations. Another reason for considering the multinormal distribution relies on the fact that it has many appealing properties: it is stable under linear transforms, zero correlation corresponds to independence, the marginals a
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Decomposition of Data Matrices by Factorsvariate or univariate devices used to reduce the dimensions of the observations. In the following three chapters, issues of reducing the dimension of a multivariate data set will be discussed. The perspectives will be different but the tools will be related.
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