V. L. Girko 
An Introduction to Statistical Analysis of Random Arrays [PDF ebook] 

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 ?m„(n) is equal to 0(n-1/2) under the condition m„n-1?c