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Non-asymptotic analysis of approxima...

Fujikoshi, Yasunori.

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Non-asymptotic analysis of approximations for multivariate statistics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Non-asymptotic analysis of approximations for multivariate statistics/ by Yasunori Fujikoshi, Vladimir V. Ulyanov.
Author:	Fujikoshi, Yasunori.
other author:	Ulyanov, Vladimir V.
Published:	Singapore :Springer Singapore : : 2020.,
Description:	ix, 130 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Multivariate analysis. -
Online resource:	https://doi.org/10.1007/978-981-13-2616-5
ISBN:	9789811326165

Non-asymptotic analysis of approximations for multivariate statistics
Fujikoshi, Yasunori.

Non-asymptotic analysis of approximations for multivariate statistics[electronic resource] /by Yasunori Fujikoshi, Vladimir V. Ulyanov. - Singapore :Springer Singapore :2020. - ix, 130 p. :ill., digital ;24 cm. - SpringerBriefs in statistics, JSS research series in statistics. - SpringerBriefs in statistics.JSS research series in statistics..

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.

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.

ISBN: 9789811326165

Standard No.: 10.1007/978-981-13-2616-5doiSubjects--Topical Terms:

517467
Multivariate analysis.

LC Class. No.: QA278 / .F855 2020

Dewey Class. No.: 519.535

Non-asymptotic analysis of approximations for multivariate statistics
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300 $a ix, 130 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in statistics, JSS research series in statistics
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.
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.
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650 2 4 $a Applied Statistics. $3 3300946
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