Titlebook: Exploring Multivariate Data with the Forward Search; Anthony C. Atkinson,Marco Riani,Andrea Cerioli Book 2004 Springer Science+Business Me

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Multivariate Transformations to Normality,are observations both close to, and far from, zero as they are for the viscosity measurements of the babyfood data introduced in §2.13.2. In this chapter we analyze such data using the multivariate version of the parametric family of power transformations introduced by Box and Cox (1964).

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B. Baljet,F. Van Der Werf,A. J. Ottoure of the outliers. The last example is the data on Swiss bank notes. We analyse all 200 observations together and also look at the two groups separately. Forward plots of individual Mahalanobis distances, calibrated by plots of a large number of units of known origin, are shown to be a powerful tool for determining group membership.

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Examples of Multivariate Data,of the data and then fit a multivariate normal distribution. This model can be fitted to any such rectangular array of numbers, so we need to explore the data to see whether this is an appropriate model for these data. Some departures from the multivariate normal model include:

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https://doi.org/10.1007/978-0-387-21840-3Boxplot; Estimator; Excel; Linear discriminant analysis; Multivariate statistics; Normal distribution; Reg

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Anthony C. Atkinson,Marco Riani,Andrea CerioliCompanion book to Robust Diagnostic Regression Analysis (ISBN 0-387-95017-6) published by Springer in 2000.Includes supplementary material:

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Otto-Joachim Grüsser,Michael HagnerUnlike the other chapters in the book, this chapter contains little data analysis. The emphasis is on theory and on the description of the search. In the first half of the chapter we provide distributional results on estimation, testing and on the distribution of quantities such as squared Mahalanobis distances from samples of size ..

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