Titlebook: A First Course in Multivariate Statistics; Bernard Flury Textbook 1997 Springer Science+Business Media New York 1997 Multivariate statisti

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https://doi.org/10.1007/978-3-658-26522-9Before introducing the multivariate normal distribution, let us briefly review some important results about the univariate normal.

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https://doi.org/10.1007/978-3-658-26522-9ng one variable at a time may not be optimal for classification purposes. Hence, it is important that we establish some terminology and acquire a basic knowledge of bivariate and multivariate distribution theory. In particular, we shall discuss notions such as independence of random variables, covar

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https://doi.org/10.1007/978-3-658-26522-9ertain distributions, and try to find out what is likely to happen and what is unlikely. Conversely, in statistics we observe data and try to find out which distribution generated the data. In the words of my colleague R.B. Fisher: “In probability, God gives us the parameters and we figure out what

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https://doi.org/10.1007/978-3-658-26522-9tly descriptive level, ignoring questions of statistical inference. The mathematical level of Chapter 5 is moderate, and all concepts are explained at great length, hoping that even students without a strong mathematical background will be able to master most of the material. Chapter 6 gives an intr

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https://doi.org/10.1007/978-3-322-82403-5 on the aspect of approximation: Given a .-variate random vector (or a “system of points in space,” in Pearson’s terminology), find an optimal approximation in a linear subspace of lower dimension. More specifically, Pearson studied the problem of fitting a line to multivariate data so as to minimiz

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,Den Endspurt intelligent managen — das ,e of normal components. An introductory example has already been given in Chapter 1 (Example 1.3) and has been discussed to some extent in Section 2.8, where the relevant terminology has been established. Before turning to the mathematical setup, let us look at yet another example to motivate the th

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