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  • 1
    Online-Ressource
    Online-Ressource
    New York, NY :Springer New York,
    UID:
    almahu_9947362744502882
    Umfang: XXX, 488 p. , online resource.
    Ausgabe: Second Edition.
    ISBN: 9780387224404
    Serie: Springer Series in Statistics,
    Inhalt: Principal component analysis is central to the study of multivariate data. Although one of the earliest multivariate techniques, it continues to be the subject of much research, ranging from new model-based approaches to algorithmic ideas from neural networks. It is extremely versatile, with applications in many disciplines. The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates and substantially expands the original version, and is once again the definitive text on the subject. It includes core material, current research and a wide range of applications. Its length is nearly double that of the first edition. Researchers in statistics, or in other fields that use principal component analysis, will find that the book gives an authoritative yet accessible account of the subject. It is also a valuable resource for graduate courses in multivariate analysis. The book requires some knowledge of matrix algebra. Ian Jolliffe is Professor of Statistics at the University of Aberdeen. He is author or co-author of over 60 research papers and three other books. His research interests are broad, but aspects of principal component analysis have fascinated him and kept him busy for over 30 years.
    Anmerkung: Mathematical and Statistical Properties of Population Principal Components -- Mathematical and Statistical Properties of Sample Principal Components -- Principal Components as a Small Number of Interpretable Variables: Some Examples -- Graphical Representation of Data Using Principal Components -- Choosing a Subset of Principal Components or Variables -- Principal Component Analysis and Factor Analysis -- Principal Components in Regression Analysis -- Principal Components Used with Other Multivariate Techniques -- Outlier Detection, Influential Observations, Stability, Sensitivity, and Robust Estimation of Principal Components -- Rotation and Interpretation of Principal Components -- Principal Component Analysis for Time Series and Other Non-Independent Data -- Principal Component Analysis for Special Types of Data -- Generalizations and Adaptations of Principal Component Analysis.
    In: Springer eBooks
    Weitere Ausg.: Printed edition: ISBN 9780387954424
    Sprache: Englisch
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 2
    Online-Ressource
    Online-Ressource
    New York, NY : Springer-Verlag New York, Inc
    UID:
    gbv_1655392948
    Umfang: Online-Ressource (XXIX, 489 p, online resource)
    Ausgabe: Second Edition
    ISBN: 9780387224404
    Serie: Springer Series in Statistics
    Inhalt: Mathematical and Statistical Properties of Population Principal Components -- Mathematical and Statistical Properties of Sample Principal Components -- Principal Components as a Small Number of Interpretable Variables: Some Examples -- Graphical Representation of Data Using Principal Components -- Choosing a Subset of Principal Components or Variables -- Principal Component Analysis and Factor Analysis -- Principal Components in Regression Analysis -- Principal Components Used with Other Multivariate Techniques -- Outlier Detection, Influential Observations, Stability, Sensitivity, and Robust Estimation of Principal Components -- Rotation and Interpretation of Principal Components -- Principal Component Analysis for Time Series and Other Non-Independent Data -- Principal Component Analysis for Special Types of Data -- Generalizations and Adaptations of Principal Component Analysis.
    Inhalt: Principal component analysis is central to the study of multivariate data. Although one of the earliest multivariate techniques, it continues to be the subject of much research, ranging from new model-based approaches to algorithmic ideas from neural networks. It is extremely versatile, with applications in many disciplines. The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates and substantially expands the original version, and is once again the definitive text on the subject. It includes core material, current research and a wide range of applications. Its length is nearly double that of the first edition. Researchers in statistics, or in other fields that use principal component analysis, will find that the book gives an authoritative yet accessible account of the subject. It is also a valuable resource for graduate courses in multivariate analysis. The book requires some knowledge of matrix algebra. Ian Jolliffe is Professor of Statistics at the University of Aberdeen. He is author or co-author of over 60 research papers and three other books. His research interests are broad, but aspects of principal component analysis have fascinated him and kept him busy for over 30 years.
    Anmerkung: Includes bibliographical references (p. [415]-457) and index
    Weitere Ausg.: ISBN 9780387954424
    Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 9780387954424
    Sprache: Englisch
    Fachgebiete: Mathematik
    RVK:
    Schlagwort(e): Hauptkomponentenanalyse
    URL: Volltext  (lizenzpflichtig)
    URL: Cover
    Mehr zum Autor: Jolliffe, Ian T.
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
    BibTip Andere fanden auch interessant ...
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