Screen LibraryAlgorithmic high-dimensional robust statistics /
"This reference text offers a clear unified treatment for graduate students, academic researchers, and professionals interested in understanding and developing statistical procedures for high-dimensional data that are robust to idealized modeling assumptions, including robustness to model missp...
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| Main Authors: | |
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| Published: |
Cambridge University Press,
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| Publisher Address: | Cambridge, United Kingdom : |
| Publication Dates: | 2023. |
| Literature type: | Book |
| Language: | English |
| Subjects: | |
| Summary: |
"This reference text offers a clear unified treatment for graduate students, academic researchers, and professionals interested in understanding and developing statistical procedures for high-dimensional data that are robust to idealized modeling assumptions, including robustness to model misspecification and to adversarial outliers in the dataset"-- |
| Carrier Form: | xvi, 283 pages : illustrations ; 24 cm |
| Bibliography: | Includes bibliographical references (pages 271-281) and index. |
| ISBN: |
9781108837811 1108837816 |
| Index Number: | QA276 |
| CLC: | O212 |
| Call Number: | O212/D536 |
| Contents: | Introduction to robust statistics -- Efficient high-dimensional robust mean estimation -- Algorithmic refinements in robust mean estimation -- Robust covariance estimation -- List-decodable learning -- Robust estimation via higher moments -- Robust supervised learning -- Information-computation trade-offs in high-dimensional robust statistics. |