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Taschenbuch. Zustand: Neu. Advanced Linear Modeling | Statistical Learning and Dependent Data | Ronald Christensen | Taschenbuch | xxiii | Englisch | 2021 | Springer | EAN 9783030291662 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Now in its third edition, this companion volume to Ronald Christensen's PlaneAnswers to Complex Questions uses three fundamental concepts from standard linear model theory-best linear prediction, projections, and Mahalanobis distance- to extendstandard linear modeling into the realms of Statistical Learning and Dependent Data. This new edition features a wealth of new and revised content. In StatisticalLearning it delves into nonparametric regression, penalized estimation (regularization),reproducing kernel Hilbert spaces, the kernel trick, and support vector machines. For Dependent Data it uses linear model theory to examine general linear models,linear mixed models, time series, spatial data, (generalized) multivariate linearmodels, discrimination, and dimension reduction. While numerous references toPlane Answers are made throughout the volume, Advanced Linear Modeling can be usedon its own given a solid background in linear models. Accompanying R code for theanalyses is available online.
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Now in its third edition, this companion volume to Ronald Christensen's PlaneAnswers to Complex Questions uses three fundamental concepts from standard linear model theory-best linear prediction, projections, and Mahalanobis distance- to extendstandard linear modeling into the realms of Statistical Learning and Dependent Data. This new edition features a wealth of new and revised content. In StatisticalLearning it delves into nonparametric regression, penalized estimation (regularization),reproducing kernel Hilbert spaces, the kernel trick, and support vector machines. For Dependent Data it uses linear model theory to examine general linear models,linear mixed models, time series, spatial data, (generalized) multivariate linearmodels, discrimination, and dimension reduction. While numerous references toPlane Answers are made throughout the volume, Advanced Linear Modeling can be usedon its own given a solid background in linear models. Accompanying R code for theanalyses is available online.
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In den WarenkorbHardcover. Zustand: Brand New. 3rd edition. 634 pages. 9.25x6.10x1.80 inches. In Stock.
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Taschenbuch. Zustand: Neu. Statistical Learning and Modeling in Data Analysis | Methods and Applications | Simona Balzano (u. a.) | Taschenbuch | viii | Englisch | 2021 | Springer | EAN 9783030699437 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | The contributions gathered in this book focus on modern methods for statistical learning and modeling in data analysis and present a series of engaging real-world applications. The book covers numerous research topics, ranging from statistical inference and modeling to clustering and factorial methods, from directional data analysis to time series analysis and small area estimation. The applications reflect new analyses in a variety of fields, including medicine, finance, engineering, marketing and cyber risk.The book gathers selected and peer-reviewed contributions presented at the 12th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2019), held in Cassino, Italy, on September 11¿13, 2019. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numerical methods, algorithms, and computational and applied results. This book, true to CLADAG¿s goals, is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification.
Sprache: Englisch
Verlag: Springer International Publishing, Springer Nature Switzerland, 2021
ISBN 10: 3030699439 ISBN 13: 9783030699437
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The contributions gathered in this book focus on modern methods for statistical learning and modeling in data analysis and present a series of engaging real-world applications. The book covers numerous research topics, ranging from statistical inference and modeling to clustering and factorial methods, from directional data analysis to time series analysis and small area estimation. The applications reflect new analyses in a variety of fields, including medicine, finance, engineering, marketing and cyber risk.The book gathers selected and peer-reviewed contributions presented at the 12th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2019), held in Cassino, Italy, on September 11-13, 2019. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numerical methods, algorithms, and computational and applied results. This book, true to CLADAG's goals, is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification.
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In den WarenkorbHardcover. Zustand: Brand New. 3rd edition. 655 pages. 10.25x7.25x1.50 inches. In Stock.
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In den WarenkorbPaperback. Zustand: Brand New. 190 pages. 9.25x6.10x0.41 inches. In Stock.