Screen LibraryLarge covariance and autocovariance matrices /
Large Covariance and Autocovariance Matrices brings together a collection of recent results on sample covariance and autocovariance matrices in high-dimensional models and novel ideas on how to use them for statistical inference in one or more high-dimensional time series models. The prerequisites i...
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| Main Authors: | |
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| Group Author: | |
| Published: |
CRC Press, Taylor & Francis Group,
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| Publisher Address: | Boca Raton, FL : |
| Publication Dates: | [2019] |
| Literature type: | Book |
| Language: | English |
| Series: |
Monographs on statistics and applied probability ;
162 |
| Subjects: | |
| Summary: |
Large Covariance and Autocovariance Matrices brings together a collection of recent results on sample covariance and autocovariance matrices in high-dimensional models and novel ideas on how to use them for statistical inference in one or more high-dimensional time series models. The prerequisites include knowledge of elementary multivariate analysis, basic time series analysis and basic results in stochastic convergence. Part I is on different methods of estimation of large covariance matrices and auto-covariance matrices and properties of these estimators. Part II covers the relevant mater |
| Carrier Form: | xxiii, 272 pages : illustrations ; 25 cm. |
| Bibliography: | Includes bibliographical references (pages 265-268) and index. |
| ISBN: |
9781138303867 1138303860 |
| Index Number: | QA188 |
| CLC: | O212.4 |
| Call Number: | O212.4/B743 |
| Contents: | Large covariance matrix I -- Large autocovariance matrix -- Spectral distribution -- Non-commutative probability -- Generalized covariance matrix I -- Generalized covariance matrix II -- Spectra of autocovariance matrix I -- Spectra of autocovariance matrix II -- Graphical inference -- Testing with trace. |