Statistical Foundations Data Science by Fan Jianqing (25 results)
Language: English
Published by T And F India 2026
- Hardcover
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Statistical Foundations of Data Science
Fan, Jianqing; Li, Runze; Zhang, Cun-Hui; Zou, Hui; Moraga, Paula
Language: English
Published by Chapman and Hall/CRC 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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Language: English
Published by CRC Press 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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Statistical Foundations of Data Science
Fan, Jianqing; Li, Runze; Zhang, Cun-Hui; Zou, Hui; Moraga, Paula
Language: English
Published by Chapman and Hall/CRC 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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Language: English
Published by CRC Press 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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Statistical Foundations of Data Science
Fan, Jianqing; Li, Runze; Zhang, Cun-Hui; Zou, Hui; Moraga, Paula
Language: English
Published by Chapman and Hall/CRC 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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Language: English
Published by Chapman and Hall/CRC 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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Statistical Foundations of Data Science
Fan, Jianqing; Li, Runze; Zhang, Cun-Hui; Zou, Hui; Moraga, Paula
Language: English
Published by Chapman and Hall/CRC 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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Language: English
Published by Chapman and Hall/CRC 2020-08-17 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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Language: English
Published by Chapman and Hall/CRC 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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Language: English
Published by Chapman and Hall/CRC 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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Language: English
Published by Taylor & Francis Inc, Bosa Roca 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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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.
Language: English
Published by Taylor & Francis Inc 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
- First Edition
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Condition: New. 2020. 1st Edition. Hardcover. . . . . .
Language: English
Published by Taylor and Francis Inc, US 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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Hardback. Condition: New. 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 mo…nograph 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.
Language: English
Published by Taylor and Francis Inc, US 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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Hardback. Condition: New. 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 mo…nograph 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.
Language: English
Published by CRC Press 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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Condition: New. The authors are international authorities and leaders on the presented topics. All are fellows of the Institute of Mathematical Statistics and the American Statistical Association. Jianqing Fan is Frederick L. Moore Professor, Princeton Uni.
Language: English
Published by Taylor & Francis Inc 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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Condition: New. 2020. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.
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Condition: Brand New. New.SoftCover International edition. Different ISBN and Cover image but contents are same as US edition.Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
Language: English
Published by Taylor and Francis Inc, US 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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Hardback. Condition: New. 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 mo…nograph 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.
Language: English
Published by Taylor and Francis Inc, US 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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Hardback. Condition: New. 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 mo…nograph 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.
Language: English
Published by Taylor & Francis Inc, Bosa Roca 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Published by T&F INDIA 2026
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- International Edition
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Hardcover. Condition: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-12 days and we do have flat rate for up to 2LB. Extra shipping charges will be requ…ested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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Language: English
Published by Chapman And Hall/CRC 2020
Series: Chapman & Hall/CRC Data Science, Book 7 of 36. Book 7 of 36 - Chapman & Hall/CRC Data Science
- Hardcover
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 mod…el selection, variable selection, machine learning, and risk management.
Language: English
Published by Chapman and Hall/CRC 2020
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Buch. Condition: Neu. Statistical Foundations of Data Science | Jianqing Fan (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2020 | Chapman and Hall/CRC | EAN 9781466510845 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
