Skip to content
Cart

Your Cart

×

You have 0 items in your cart.

Register Sign in Wishlist

Large Sample Covariance Matrices and High-Dimensional Data Analysis

$85.00 (P)

Part of Cambridge Series in Statistical and Probabilistic Mathematics

  • Date Published: March 2015
  • availability: Temporarily unavailable - available from TBC
  • format: Hardback
  • isbn: 9781107065178

$ 85.00 (P)
Hardback

Temporarily unavailable - available from TBC
Notify me when available Add to wishlist

Other available formats:
eBook


Looking for an examination copy?

If you are interested in the title for your course we can consider offering an examination copy. To register your interest please contact collegesales@cambridge.org providing details of the course you are teaching.

Description
Product filter button
Description
Contents
Resources
Courses
About the Authors
  • 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.

    • Exposes the reader to recent advances in the field of high-dimensional statistics
    • Almost all of the new tools and results presented in the book are a result of the authors' own research with their collaborators
    • Is the first book-length exploration of new tools for high-dimensional statistics that are derived from the theory of random matrices
    Read more

    Reviews & endorsements

    "This is the first book which treats systematic corrections to the classical multivariate statistical procedures so that the resultant procedures can be used for high-dimensional data. The corrections have been done by employing asymptotic tools based on the theory of random matrices."
    Yasunori Fujikoshi, Hiroshima University

    Customer reviews

    Not yet reviewed

    Be the first to review

    Review was not posted due to profanity

    ×

    , create a review

    (If you're not , sign out)

    Please enter the right captcha value
    Please enter a star rating.
    Your review must be a minimum of 12 words.

    How do you rate this item?

    ×

    Product details

    • Date Published: March 2015
    • format: Hardback
    • isbn: 9781107065178
    • length: 322 pages
    • dimensions: 262 x 183 x 23 mm
    • weight: 0.75kg
    • contains: 80 b/w illus. 30 tables
    • availability: Temporarily unavailable - available from TBC
  • Table of Contents

    1. Introduction
    2. Limiting spectral distributions
    3. CLT for linear spectral statistics
    4. The generalised variance and multiple correlation coefficient
    5. The T2-statistic
    6. Classification of data
    7. Testing the general linear hypothesis
    8. Testing independence of sets of variates
    9. Testing hypotheses of equality of covariance matrices
    10. Estimation of the population spectral distribution
    11. Large-dimensional spiked population models
    12. Efficient optimisation of a large financial portfolio.

  • Authors

    Jianfeng Yao, The University of Hong Kong
    Jianfeng Yao has rich research experience in random matrix theory and its applications to high-dimensional statistics. In recent years, he has published many authoritative papers in these areas and organised several international workshops on related topics.

    Shurong Zheng, Northeast Normal University, China
    Shurong Zheng is author of several influential results in random matrix theory including a widely used central limit theorem for eigenvalue statistics of a random Fisher matrix. She has also developed important applications of the inference theory presented in the book to real-life high-dimensional statistics.

    Zhidong Bai, Northeast Normal University, China
    Zhidong Bai is a world-leading expert in random matrix theory and high-dimensional statistics. He has published over 200 research papers and several specialized monographs, including Spectral Analysis of Large Dimensional Random Matrices (with J. W. Silverstein), for which he won the Natural Science Award of China (Second Class).

Sign In

Please sign in to access your account

Cancel

Not already registered? Create an account now. ×

Sorry, this resource is locked

Please register or sign in to request access. If you are having problems accessing these resources please email lecturers@cambridge.org

Register Sign in
Please note that this file is password protected. You will be asked to input your password on the next screen.

» Proceed

You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.

Continue ×

Continue ×

Continue ×

Find content that relates to you

Are you sure you want to delete your account?

This cannot be undone.

Cancel

Thank you for your feedback which will help us improve our service.

If you requested a response, we will make sure to get back to you shortly.

×
Please fill in the required fields in your feedback submission.
×