9781466510845 - statistical foundations of data science (chapman & hall/crc data science series) de fan, jianqing; li, runze; zhang, cun-hui; zou, hui (25 resultados)
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Editorial: Chapman and Hall/CRC 2020-08-17, 2020
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Statistical Foundations of Data Science
Fan, Jianqing; Li, Runze; Zhang, Cun-Hui; Zou, Hui; Moraga, Paula
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2020
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Statistical Foundations of Data Science
Fan, Jianqing; Li, Runze; Zhang, Cun-Hui; Zou, Hui; Moraga, Paula
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2020
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Statistical Foundations of Data Science
Fan, Jianqing; Li, Runze; Zhang, Cun-Hui; Zou, Hui; Moraga, Paula
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2020
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Editorial: Taylor & Francis Inc, 2020
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Condición: New. 2020. 1st Edition. Hardcover. . . . . .
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Editorial: Chapman and Hall/CRC, 2020
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Statistical Foundations of Data Science
Fan, Jianqing; Li, Runze; Zhang, Cun-Hui; Zou, Hui; Moraga, Paula
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2020
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Hardcover. Condición: 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.
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Idioma: Inglés
Editorial: Taylor and Francis Inc, US, 2020
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Hardback. Condición: 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.
Idioma: Inglés
Editorial: Taylor & Francis Inc, 2020
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Condición: New. 2020. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.
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Idioma: Inglés
Editorial: Taylor and Francis Inc, US, 2020
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Hardback. Condición: 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.
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Idioma: Inglés
Editorial: CRC Press, 2020
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Condición: 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.
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Idioma: Inglés
Editorial: Taylor and Francis Inc, US, 2020
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Hardback. Condición: 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.
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Idioma: Inglés
Editorial: Taylor and Francis Inc, US, 2020
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Hardback. Condición: 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.
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Editorial: Taylor & Francis Inc, Bosa Roca, 2020
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Hardcover. Condición: 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.
Editorial: Chapman & Hall, 2020
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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Hardcover. Condición: Brand New. 600 pages. 9.50x6.25x1.50 inches. In Stock.
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Idioma: Inglés
Editorial: Chapman And Hall/CRC, 2020
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Buch. Condición: 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.
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Buch. Condición: 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.
Editorial: Chapman & Hall, 2020
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Hardcover. Condición: Brand New. 600 pages. 9.50x6.25x1.50 inches. In Stock. This item is printed on demand.
