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.
表中的内容
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.
关于作者
Fujikoshi, Yasunori, Hiroshima University, Higashi-Hiroshima, Japan
Ulyanov, Vladimir V., Moscow State University and HSE University, Moscow, Russia
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