Non-Asymptotic Analysis of Approximations for Multivariate Statistics

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...

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Bibliographic Details
Main Authors: Fujikoshi, Yasunori, Ulyanov, Vladimir V. (Author)
Format: eBook
Language:English
Published: Singapore Springer Nature Singapore 2020, 2020
Edition:1st ed. 2020
Series:JSS Research Series in Statistics
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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245 0 0 |a Non-Asymptotic Analysis of Approximations for Multivariate Statistics  |h Elektronische Ressource  |c by Yasunori Fujikoshi, Vladimir V. Ulyanov 
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260 |a Singapore  |b Springer Nature Singapore  |c 2020, 2020 
300 |a IX, 130 p. 16 illus  |b online resource 
505 0 |a 1. Introduction -- 2. Correlation Coefficient -- 3. MANOVA Test Statistics -- 4. Linear and Quadratic Discriminant Functions -- 5. Bootstrap Confidence Sets -- 6. Gaussian Comparison -- 7. Cornish-Fisher Expansions -- 8 Approximations for Statistics Based on Random Sample Sizes -- 9. Power-divergence Statistics -- 10.General Approach to Construct Non-asymptotic Bounds -- 11 - Other Topics -- Index 
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653 |a Statistics  
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653 |a Applied Statistics 
653 |a Statistics and Computing 
700 1 |a Ulyanov, Vladimir V.  |e [author] 
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520 |a 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.