An Introduction to Statistical Analysis of Random Arrays / / V. L. Girko.
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Place / Publishing House: | Berlin ;, Boston : : De Gruyter, , [2018] ©1998 |
Year of Publication: | 2018 |
Edition: | Reprint 2018 |
Language: | English |
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Physical Description: | 1 online resource (673 p.) |
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Girko, V. L., author. aut http://id.loc.gov/vocabulary/relators/aut An Introduction to Statistical Analysis of Random Arrays / V. L. Girko. Reprint 2018 Berlin ; Boston : De Gruyter, [2018] ©1998 1 online resource (673 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Frontmatter -- CONTENTS -- List of basic notations and assumptions -- Preface and some historical remarks -- Chapter 1. Introduction to the theory of sample matrices of fixed dimension -- Chapter 2. Canonical equations -- Chapter 3. The First Law for the eigenvalues and eigenvectors of random symmetric matrices -- Chapter 4. The Second Law for the singular values and eigenvectors of random matrices. Inequalities for the spectral radius of large random matrices -- Chapter 5. The Third Law for the eigenvalues and eigenvectors of empirical covariance matrices -- Chapter 6. The first proof of the Strong Circular Law -- Chapter 7. Strong Law for normalized spectral functions of nonselfadjoint random matrices with independent row vectors and simple rigorous proof of the Strong Circular Law -- Chapter 8. Rigorous proof of the Strong Elliptic Law -- Chapter 9. The Circular and Uniform Laws for eigenvalues of random nonsymmetric complex matrices with independent entries -- Chapter 10. Strong V-Law for eigenvalues of nonsymmetric random matrices -- Chapter 11. Convergence rate of the expected spectral functions of symmetric random matrices is equal to 0(n-1/2) -- Chapter 12. Convergence rate of expected spectral functions of the sample covariance matrix Ȓm„(n) is equal to 0(n-1/2) under the condition m„n-1≤c<1 -- Chapter 13. The First Spacing Law for random symmetric matrices -- Chapter 14. Ten years of General Statistical Analysis (The main G-estimators of General Statistical Analysis) -- References -- Index restricted access http://purl.org/coar/access_right/c_16ec online access with authorization star Mode of access: Internet via World Wide Web. In English. Description based on online resource; title from PDF title page (publisher's Web site, viewed 29. Nov 2021) Statistik. Zufallsgröße. MATHEMATICS / Probability & Statistics / General. bisacsh Title is part of eBook package: De Gruyter DGBA Mathematics - 1990 - 1999 9783110637199 ZDB-23-GMA print 9783110354775 https://doi.org/10.1515/9783110916683 https://www.degruyter.com/isbn/9783110916683 Cover https://www.degruyter.com/document/cover/isbn/9783110916683/original |
language |
English |
format |
eBook |
author |
Girko, V. L., Girko, V. L., |
spellingShingle |
Girko, V. L., Girko, V. L., An Introduction to Statistical Analysis of Random Arrays / Frontmatter -- CONTENTS -- List of basic notations and assumptions -- Preface and some historical remarks -- Chapter 1. Introduction to the theory of sample matrices of fixed dimension -- Chapter 2. Canonical equations -- Chapter 3. The First Law for the eigenvalues and eigenvectors of random symmetric matrices -- Chapter 4. The Second Law for the singular values and eigenvectors of random matrices. Inequalities for the spectral radius of large random matrices -- Chapter 5. The Third Law for the eigenvalues and eigenvectors of empirical covariance matrices -- Chapter 6. The first proof of the Strong Circular Law -- Chapter 7. Strong Law for normalized spectral functions of nonselfadjoint random matrices with independent row vectors and simple rigorous proof of the Strong Circular Law -- Chapter 8. Rigorous proof of the Strong Elliptic Law -- Chapter 9. The Circular and Uniform Laws for eigenvalues of random nonsymmetric complex matrices with independent entries -- Chapter 10. Strong V-Law for eigenvalues of nonsymmetric random matrices -- Chapter 11. Convergence rate of the expected spectral functions of symmetric random matrices is equal to 0(n-1/2) -- Chapter 12. Convergence rate of expected spectral functions of the sample covariance matrix Ȓm„(n) is equal to 0(n-1/2) under the condition m„n-1≤c<1 -- Chapter 13. The First Spacing Law for random symmetric matrices -- Chapter 14. Ten years of General Statistical Analysis (The main G-estimators of General Statistical Analysis) -- References -- Index |
author_facet |
Girko, V. L., Girko, V. L., |
author_variant |
v l g vl vlg v l g vl vlg |
author_role |
VerfasserIn VerfasserIn |
author_sort |
Girko, V. L., |
title |
An Introduction to Statistical Analysis of Random Arrays / |
title_full |
An Introduction to Statistical Analysis of Random Arrays / V. L. Girko. |
title_fullStr |
An Introduction to Statistical Analysis of Random Arrays / V. L. Girko. |
title_full_unstemmed |
An Introduction to Statistical Analysis of Random Arrays / V. L. Girko. |
title_auth |
An Introduction to Statistical Analysis of Random Arrays / |
title_alt |
Frontmatter -- CONTENTS -- List of basic notations and assumptions -- Preface and some historical remarks -- Chapter 1. Introduction to the theory of sample matrices of fixed dimension -- Chapter 2. Canonical equations -- Chapter 3. The First Law for the eigenvalues and eigenvectors of random symmetric matrices -- Chapter 4. The Second Law for the singular values and eigenvectors of random matrices. Inequalities for the spectral radius of large random matrices -- Chapter 5. The Third Law for the eigenvalues and eigenvectors of empirical covariance matrices -- Chapter 6. The first proof of the Strong Circular Law -- Chapter 7. Strong Law for normalized spectral functions of nonselfadjoint random matrices with independent row vectors and simple rigorous proof of the Strong Circular Law -- Chapter 8. Rigorous proof of the Strong Elliptic Law -- Chapter 9. The Circular and Uniform Laws for eigenvalues of random nonsymmetric complex matrices with independent entries -- Chapter 10. Strong V-Law for eigenvalues of nonsymmetric random matrices -- Chapter 11. Convergence rate of the expected spectral functions of symmetric random matrices is equal to 0(n-1/2) -- Chapter 12. Convergence rate of expected spectral functions of the sample covariance matrix Ȓm„(n) is equal to 0(n-1/2) under the condition m„n-1≤c<1 -- Chapter 13. The First Spacing Law for random symmetric matrices -- Chapter 14. Ten years of General Statistical Analysis (The main G-estimators of General Statistical Analysis) -- References -- Index |
title_new |
An Introduction to Statistical Analysis of Random Arrays / |
title_sort |
an introduction to statistical analysis of random arrays / |
publisher |
De Gruyter, |
publishDate |
2018 |
physical |
1 online resource (673 p.) |
edition |
Reprint 2018 |
contents |
Frontmatter -- CONTENTS -- List of basic notations and assumptions -- Preface and some historical remarks -- Chapter 1. Introduction to the theory of sample matrices of fixed dimension -- Chapter 2. Canonical equations -- Chapter 3. The First Law for the eigenvalues and eigenvectors of random symmetric matrices -- Chapter 4. The Second Law for the singular values and eigenvectors of random matrices. Inequalities for the spectral radius of large random matrices -- Chapter 5. The Third Law for the eigenvalues and eigenvectors of empirical covariance matrices -- Chapter 6. The first proof of the Strong Circular Law -- Chapter 7. Strong Law for normalized spectral functions of nonselfadjoint random matrices with independent row vectors and simple rigorous proof of the Strong Circular Law -- Chapter 8. Rigorous proof of the Strong Elliptic Law -- Chapter 9. The Circular and Uniform Laws for eigenvalues of random nonsymmetric complex matrices with independent entries -- Chapter 10. Strong V-Law for eigenvalues of nonsymmetric random matrices -- Chapter 11. Convergence rate of the expected spectral functions of symmetric random matrices is equal to 0(n-1/2) -- Chapter 12. Convergence rate of expected spectral functions of the sample covariance matrix Ȓm„(n) is equal to 0(n-1/2) under the condition m„n-1≤c<1 -- Chapter 13. The First Spacing Law for random symmetric matrices -- Chapter 14. Ten years of General Statistical Analysis (The main G-estimators of General Statistical Analysis) -- References -- Index |
isbn |
9783110916683 9783110637199 9783110354775 |
url |
https://doi.org/10.1515/9783110916683 https://www.degruyter.com/isbn/9783110916683 https://www.degruyter.com/document/cover/isbn/9783110916683/original |
illustrated |
Not Illustrated |
dewey-hundreds |
500 - Science |
dewey-tens |
510 - Mathematics |
dewey-ones |
519 - Probabilities & applied mathematics |
dewey-full |
519.2 |
dewey-sort |
3519.2 |
dewey-raw |
519.2 |
dewey-search |
519.2 |
doi_str_mv |
10.1515/9783110916683 |
oclc_num |
1076460161 |
work_keys_str_mv |
AT girkovl anintroductiontostatisticalanalysisofrandomarrays AT girkovl introductiontostatisticalanalysisofrandomarrays |
status_str |
n |
ids_txt_mv |
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carrierType_str_mv |
cr |
hierarchy_parent_title |
Title is part of eBook package: De Gruyter DGBA Mathematics - 1990 - 1999 |
is_hierarchy_title |
An Introduction to Statistical Analysis of Random Arrays / |
container_title |
Title is part of eBook package: De Gruyter DGBA Mathematics - 1990 - 1999 |
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1770178044679421952 |
fullrecord |
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