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Publicado por Wiley, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Libro
Condición: New.
Publicado por John Wiley & Sons Inc, New York, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: Grand Eagle Retail, Wilmington, DE, Estados Unidos de America
Libro
Hardcover. Condición: new. Hardcover. Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented. Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications. Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like: New ways to plot large sets of time seriesAn automatic procedure to build univariate ARMA models for individual components of a large data setPowerful outlier detection procedures for large sets of related time seriesNew methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time seriesBroad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor modelsDiscussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time seriesForecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting.Introduction of modern procedures for modeling and forecasting spatio-temporal data Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Publicado por Wiley, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: booksXpress, Bayonne, NJ, Estados Unidos de America
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Hardcover. Condición: new.
Publicado por John Wiley and Sons, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: INDOO, Avenel, NJ, Estados Unidos de America
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Condición: New. Brand New.
Publicado por Wiley, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Condición: As New. Unread book in perfect condition.
Publicado por Wiley, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: GreatBookPricesUK, Castle Donington, DERBY, Reino Unido
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Condición: New.
Publicado por Wiley 2021-06-11, Hoboken, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: Blackwell's, London, Reino Unido
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hardback. Condición: New. Language: ENG.
Publicado por Wiley-Blackwell, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
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HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Publicado por Wiley, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: Monster Bookshop, Fleckney, Reino Unido
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Hardcover. Condición: New. BRAND NEW ** SUPER FAST SHIPPING FROM UK WAREHOUSE ** 30 DAY MONEY BACK GUARANTEE.
Publicado por Wiley, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Condición: New. In.
Publicado por Wiley, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: GreatBookPricesUK, Castle Donington, DERBY, Reino Unido
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Condición: As New. Unread book in perfect condition.
Publicado por John Wiley & Sons Inc, New York, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: AussieBookSeller, Truganina, VIC, Australia
Libro
Hardcover. Condición: new. Hardcover. Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented. Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications. Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like: New ways to plot large sets of time seriesAn automatic procedure to build univariate ARMA models for individual components of a large data setPowerful outlier detection procedures for large sets of related time seriesNew methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time seriesBroad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor modelsDiscussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time seriesForecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting.Introduction of modern procedures for modeling and forecasting spatio-temporal data Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Publicado por John Wiley & Sons Inc, New York, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: CitiRetail, Stevenage, Reino Unido
Libro
Hardcover. Condición: new. Hardcover. Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented. Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications. Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like: New ways to plot large sets of time seriesAn automatic procedure to build univariate ARMA models for individual components of a large data setPowerful outlier detection procedures for large sets of related time seriesNew methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time seriesBroad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor modelsDiscussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time seriesForecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting.Introduction of modern procedures for modeling and forecasting spatio-temporal data Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Publicado por John Wiley & Sons, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: moluna, Greven, Alemania
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Gebunden. Condición: New.
Publicado por Wiley, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: Palexbooks, Miami, FL, Estados Unidos de America
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Condición: New. Brand new! Please provide a physical shipping address.
Publicado por Wiley, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: GoldenWavesOfBooks, Fayetteville, TX, Estados Unidos de America
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Hardcover. Condición: new. New. Fast Shipping and good customer service.
Publicado por Wiley, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: Grumpys Fine Books, Tijeras, NM, Estados Unidos de America
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Hardcover. Condición: new. Prompt service guaranteed.
Publicado por John Wiley & Sons Inc, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: Revaluation Books, Exeter, Reino Unido
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Hardcover. Condición: Brand New. 560 pages. 10.25x7.25x1.25 inches. In Stock.
Publicado por Wiley, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: Wizard Books, Long Beach, CA, Estados Unidos de America
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Hardcover. Condición: new. New.
Publicado por Wiley, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: Big Bill's Books, Wimberley, TX, Estados Unidos de America
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Hardcover. Condición: new. Brand New Copy.
Publicado por Wiley, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: GoldenDragon, Houston, TX, Estados Unidos de America
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Hardcover. Condición: new. Buy for Great customer experience.
Publicado por Wiley, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: Front Cover Books, Denver, CO, Estados Unidos de America
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Condición: new.
Publicado por John Wiley & Sons Inc, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: Kennys Bookstore, Olney, MD, Estados Unidos de America
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Condición: New. 2021. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.
Publicado por Wiley, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: Toscana Books, AUSTIN, TX, Estados Unidos de America
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Hardcover. Condición: very good. Purchase pre-owned books for prompt service and customer satisfaction.
Publicado por Wiley, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: GoldBooks, Denver, CO, Estados Unidos de America
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Hardcover. Condición: new. New Copy. Customer Service Guaranteed.
Publicado por John Wiley & Sons Inc, 2021
ISBN 10: 1119417384ISBN 13: 9781119417385
Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
Libro Original o primera edición
Condición: New. 2021. 1st Edition. Hardcover. . . . . .