Titlebook: Applied Multivariate Statistical Analysis; Wolfgang Härdle,Léopold Simar Textbook 20072nd edition Springer-Verlag Berlin Heidelberg 2007 A

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https://doi.org/10.1007/978-3-662-01075-4tools were based on either univariate (bivariate) data representations or on “slick” transformations of multivariate information perceivable by the human eye. Most of the tools are extremely useful in a modelling step, but unfortunately, do not give the full picture of the data set. One reason for t

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https://doi.org/10.1007/978-3-658-40755-1variate 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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https://doi.org/10.1007/978-3-658-40755-1 Principal components analysis has the same objective with the exception that the rows of the data matrix . will now be considered as observations from a .-variate random variable .. The principle idea of reducing the dimension of . is achieved through linear combinations. Low dimensional linear com

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https://doi.org/10.1007/978-3-658-41831-1situations can arise. Given a data set containing measurements on individuals, in some cases we want to see if some natural groups or classes of individuals exist, and in other cases, we want to classify the individuals according to a set of existing groups. Cluster analysis develops tools and metho

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https://doi.org/10.1007/978-3-031-36043-5 observations, into these known groups. For instance, in credit scoring, a bank knows from past experience that there are good customers (who repay their loan without any problems) and bad customers (who showed difficulties in repaying their loan). When a new customer asks for a loan, the bank has t