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  1. Multivariate analysis probability and mathematical statistics.
  2. Multivariate Analysis with Optimal Scaling.
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About this book A comprehensive examination of high-dimensional analysis of multivariate methods and their real-world applications Multivariate Statistics: High-Dimensional and Large-Sample Approximations is the first book of its kind to explore how classical multivariate methods can be revised and used in place of conventional statistical tools. Subsequent chapters feature additional topical coverage including: High-dimensional approximations of various statistics High-dimensional statistical methods Approximations with computable error bound Selection of variables based on model selection approach Statistics with error bounds and their appearance in discriminant analysis, growth curve models, generalized linear models, profile analysis, and multiple comparison Each chapter provides real-world applications and thorough analyses of the real data.

Reviews "The book is designed for readers interested in multivariate analysis with a good background in matrix algebra, mathematical statistical inference and probability theory. Its contents are, in general, well organised and the intuitive ideas behind the different multivariate methods, the asymptotic expansion techniques and the calculation of error bounds using scale mixtures, are well expressed.

The mathematical proofs are well presented and selected and I have found the mathematical appendices to be very useful as guides to following the proofs. He has authored over journal articles in the area of multivariate analysis. Summary PDF Request permissions. Tools Get online access For authors.

Multivariate Analysis (Probability And Mathematical Statistics)

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