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Synopsis
This book gives a comprehensive and systematic account of high-dimensional data analysis, including variable selection via regularization methods and sure independent feature screening methods. Offering more details on the topics than similar books, it is a valuable reference for researchers involved with model selection, variable selection, machine learning, and risk management. The book can also be used as a text for graduate and senior undergraduate students.
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- PublisherChapman and Hall/CRC
- Publication date2020
- ISBN 10 1466510846
- ISBN 13 9781466510845
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages774
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Statistical Foundations of Data Science (Chapman & Hall/CRC Data Science Series)
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Statistical Foundations of Data Science (Chapman & Hall/CRC Data Science Series)
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Statistical Foundations of Data Science
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Statistical Foundations of Data Science (Hardcover)
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Taylor & Francis Inc, Bosa Roca, 2020
ISBN 10: 1466510846
ISBN 13: 9781466510845
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Hardcover. Condition: new. Hardcover. Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications.The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning. Gives a comprehensive and systematic account of high-dimensional data analysis, including variable selection via regularization methods and sure independent feature screening methods. It is a valuable reference for researchers involved with model selection, variable selection, machine learning, and risk management. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781466510845
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Statistical Foundations of Data Science (Chapman & Hall/CRC Data Science Series)
Published by
Chapman and Hall/CRC 2020-06-29, 2020
ISBN 10: 1466510846
ISBN 13: 9781466510845
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Hardcover
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Statistical Foundations of Data Science (Chapman & Hall/CRC Data Science Series)
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Statistical Learning for High-Dimen
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Statistical Foundations of Data Science (Chapman & Hall/CRC Data Science Series)
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Statistical Learning for High-Dimen
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Statistical Foundations of Data Science
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