Titlebook: Applied Multivariate Statistical Analysis; Wolfgang Karl Härdle,Léopold Simar Textbook 20195th edition Springer Nature Switzerland AG 2019

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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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Theory of Estimation generates data. This is known as statistical inference: we infer from information contained in sample properties of the population from which the observations are taken. In multivariate statistical inference, we do exactly the same.

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Variable Selectionorrectly identify the relevant variables, that is, to recover the correct model under given assumptions. It is known that under certain conditions, the ordinary least squares (OLS) method produces poor prediction results and does not yield a parsimonious model causing overfitting. Therefore the obje

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Occupation

Principal Components Analysis. 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.

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Discriminant Analysis 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

Epidural-Space