Large Covariance and Autocovariance Matrices (eBook)

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1. LARGE COVARIANCE MATRIX I Consistency

Covariance classes and regularization

Covariance classes

Covariance regularization

Bandable Sp

Parameter space

Estimation in U

Minimaxity

Toeplitz Sp

Parameter space

Estimation in Gß (M ) or Fß (M0, M )

Minimaxity

Sparse Sp

Parameter space

Estimation in Ut (q, C0(p), M ) or Gq (Cn,p)

Minimaxity

2. LARGE COVARIANCE MATRIX II

Bandable Sp

Models and examples

Weak dependence

Estimation

Sparse Sp

3. LARGE AUTOCOVARIANCE MATRIX

Models and examples

Estimation of G0,p

Estimation of Gu,p

Parameter spaces

Estimation

Estimation in MA(r)

Estimation in IVAR(r)

Gaussian assumption

Simulations

Part II

4. SPECTRAL DISTRIBUTION

LSD

Moment method

Method of Stieltjes transform

Wigner matrix: semi-circle law

Independent matrix: Mar¿cenko-Pastur law

> 0

Results on Z: p/n ¿ 0

5. NON-COMMUTATIVE PROBABILITY

NCP and its convergence

Essentials of partition theory

M¿obius function

Partition and non-crossing partition

Kreweras complement

Free cumulant; free independence

Moments of free variables

Joint convergence of random matrices

Compound free Poisson

6. GENERALIZED COVARIANCE MATRIX I

Preliminaries

Assumptions

Embedding

NCP convergence

Main idea

Main convergence

LSD of symmetric polynomials

Stieltjes transform

Corollaries

7. GENERALIZED COVARIANCE MATRIX II

Preliminaries

Assumptions

Centering and Scaling

Main idea

NCP convergence

LSD of symmetric polynomials

Stieltjes transform

Corollaries

8. SPECTRA OF AUTOCOVARIANCE MATRIX I

Assumptions

LSD when p/n ¿ y ¿ (0, 8)

MA(q), q < 8

MA(8)

Application to specific cases

LSD when p/n ¿ 0

Application to specific cases

Non-symmetric polynomials

9. SPECTRA OF AUTOCOVARIANCE MATRIX II

Assumptions

LSD when p/n ¿ y ¿ (0, 8)

MA(q), q < 8

MA(8)

LSD when p/n ¿ 0

MA(q), q < 8

MA(8)

10. GRAPHICAL INFERENCE

MA order determination

AR order determination

Graphical tests for parameter matrices

11. TESTING WITH TRACE

One sample trace

Two sample trace

Testing

12. SUPPLEMENTARY PROOFS

Proof of Lemma

Proof of Theorem (a)

Proof of Theorem

Proof of Lemma

Proof of Corollary (c)

Proof of Corollary (c)

Proof of Corollary (c)

Proof of Lemma

Proof of Lemma

Lemmas for Theorem

1. LARGE COVARIANCE MATRIX I Consistency

Covariance classes and regularization

Covariance classes

Covariance regularization

Bandable Sp

Parameter space

Estimation in U

Minimaxity

Toeplitz Sp

Parameter space

Estimation in Gß (M ) or Fß (M0, M )

Minimaxity

Sparse Sp

Parameter space

Estimation in Ut (q, C0(p), M ) or Gq (Cn,p)

Minimaxity

2. LARGE COVARIANCE MATRIX II

Bandable Sp

Models and examples

Weak dependence

Estimation

Sparse Sp

3. LARGE AUTOCOVARIANCE MATRIX

Models and examples

Estimation of G0,p

Estimation of Gu,p

Parameter spaces

Estimation

Estimation in MA(r)

Estimation in IVAR(r)

Gaussian assumption

Simulations

Part II

4. SPECTRAL DISTRIBUTION

LSD

Moment method

Method of Stieltjes transform

Wigner matrix: semi-circle law

Independent matrix: Mar¿cenko-Pastur law

> 0

Results on Z: p/n ¿ 0

5. NON-COMMUTATIVE PROBABILITY

NCP and its convergence

Essentials of partition theory

M¿obius function

Partition and non-crossing partition

Kreweras complement

Free cumulant; free independence

Moments of free variables

Joint convergence of random matrices

Compound free Poisson

6. GENERALIZED COVARIANCE MATRIX I

Preliminaries

Assumptions

Embedding

NCP convergence

Main idea

Main convergence

LSD of symmetric polynomials

Stieltjes transform

Corollaries

7. GENERALIZED COVARIANCE MATRIX II

Preliminaries

Assumptions

Centering and Scaling

Main idea

NCP convergence

LSD of symmetric polynomials

Stieltjes transform

Corollaries

8. SPECTRA OF AUTOCOVARIANCE MATRIX I

Assumptions

LSD when p/n ¿ y ¿ (0, 8)

MA(q), q < 8

MA(8)

Application to specific cases

LSD when p/n ¿ 0

Application to specific cases

Non-symmetric polynomials

9. SPECTRA OF AUTOCOVARIANCE MATRIX II

Assumptions

LSD when p/n ¿ y ¿ (0, 8)

MA(q), q < 8

MA(8)

LSD when p/n ¿ 0

MA(q), q < 8

MA(8)

10. GRAPHICAL INFERENCE

MA order determination

AR order determination

Graphical tests for parameter matrices

11. TESTING WITH TRACE

One sample trace

Two sample trace

Testing

12. SUPPLEMENTARY PROOFS

Proof of Lemma

Proof of Theorem (a)

Proof of Theorem

Proof of Lemma

Proof of Corollary (c)

Proof of Corollary (c)

Proof of Corollary (c)

Proof of Lemma

Proof of Lemma

Lemmas for Theorem

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Verlag Taylor & Francis Ebooks
Einband PDF
Erscheinungsjahr 2018
Seitenangabe 296 S.
Ausgabekennzeichen Englisch
Auflage 18001 A. 1. Auflage
Plattform PDF
Autor Bose, Arup / Bhattacharjee, Monika

Über den Autor Arup Bose

Arup Bose is an Honorary Visiting Professor at the Indian Statistical Institute since his superannuation in 2024. He has published more than 150 research articles in probability, statistics, econometrics and economics., as well as six books (singly or with others) covering topics in random matrices, non-commutative probability, U-statistics, Mm estimates, resampling, and martingales. He is a Fellow of the Institute of Mathematical Statistics, the Indian National Science Academy, the National Academy of Science and the Indian Academy of Sciences. He has won the Shanti Swarup Bhatnagar Prize and the C.R. Rao award from the Governemtn of India, and the Mahalanobis International Award for Lifetime Achievements from the International Statistical Institute.

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