SpringerBriefs in Statistics Non-Asymptotic Analysis of Approximations for Multivariate Statistics - Yasunori Fujikoshi Vladimir V Ulyanov Kartoniert (TB)

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. Las tly 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.