Large Sample Covariance Matrices and High Dimensional Data Analysis 1st edition by Jianfeng Yao, Shurong Zheng, Zhidong Bai ISBN 1107065178 978-1107065178

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ISBN 10: 1107065178
ISBN 13: 978-1107065178
Author: Jianfeng Yao, Shurong Zheng, Zhidong Bai

High-dimensional data appear in many fields, and their analysis has become increasingly important in modern statistics. However, it has long been observed that several well-known methods in multivariate analysis become inefficient, or even misleading, when the data dimension p is larger than, say, several tens. A seminal example is the well-known inefficiency of Hotelling’s T2-test in such cases. This example shows that classical large sample limits may no longer hold for high-dimensional data; statisticians must seek new limiting theorems in these instances. Thus, the theory of random matrices (RMT) serves as a much-needed and welcome alternative framework. Based on the authors’ own research, this book provides a first-hand introduction to new high-dimensional statistical methods derived from RMT. The book begins with a detailed introduction to useful tools from RMT, and then presents a series of high-dimensional problems with solutions provided by RMT methods.

Large Sample Covariance Matrices and High Dimensional Data Analysis 1st Table of contents:

Chapter1 : Introduction

Chapter 2: Content

Chapter 3: Conclusion 

Chapter 4: Appendices

Chapter 5: Glossary

Chapter 6: References 

Chapter 7: Index

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Tags: Jianfeng Yao, Shurong Zheng, Zhidong Bai, Large Sample, Data Analysis