Non-Asymptotic Analysis of Approximations for Multivariate Statistics
Jun 2020 · Springer Nature
Ebook
130
Pages
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About this ebook
This book presents recent non-asymptotic results for approximations in multivariate statistical analysis. The book is unique in its focus on results with the correct error structure for all the parameters involved. Firstly, it discusses the computable error bounds on correlation coefficients, MANOVA tests and discriminant functions studied in recent papers. It then introduces new areas of research in high-dimensional approximations for bootstrap procedures, Cornish–Fisher expansions, power-divergence statistics and approximations of statistics based on observations with random sample size. Lastly, it proposes a general approach for the construction of non-asymptotic bounds, providing relevant examples for several complicated statistics. It is a valuable resource for researchers with a basic understanding of multivariate statistics.
About the author
Fujikoshi, Yasunori, Hiroshima University, Higashi-Hiroshima, Japan
Ulyanov, Vladimir V., Moscow State University and HSE University, Moscow, Russia
Ulyanov, Vladimir V., Moscow State University and HSE University, Moscow, Russia
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