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Non-Parametric Statistical Diagnosis
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Recensione
`Overall, the book is nicely organized, and the material is clearly presented. The book has several strengths. I found Non-Parametric Statistical Diagnosis to be an interesting book to add to the area of change-point analysis.'
Journal of the American Statistical Association, September 2001
Contenuti
Preface. Part 1: Theory. 1. Preliminary considerations. 2. State of the art review. 3. Retrospective methods of statistical diagnosis for random sequences: change-point problems. 4. Retrospective methods of statistical diagnosis for random processes: `Contamination' problems. 5. Sequential methods of statistical diagnosis. 6. Statistical diagnosis problems for random fields. Part 2: Applications. 7. Application of the change-point analysis to investigation of the brain electrical activity. 8. Methods of statistical diagnosis in economic and financial systems. Appendix. Bibliography. Author Index. Subject Index. Main Notations and Abbreviations.
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Non-Parametric Statistical Diagnosis (eng)
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Non-Parametric Statistical Diagnosis|This book has a distinct philosophy and it is appropriate to make it explicit at the outset. In our view almost all classic statistical inference is based upon the assumption (explicit or implicit) that there exis. Codice articolo 5819321
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Non-parametric Statistical Diagnosis : Problems and Methods
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Non-Parametric Statistical Diagnosis
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Taschenbuch. Condizione: Neu. Non-Parametric Statistical Diagnosis | Problems and Methods | E. Brodsky (u. a.) | Taschenbuch | xv | Englisch | 2010 | Springer | EAN 9789048154654 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Codice articolo 107246051
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book has a distinct philosophy and it is appropriate to make it explicit at the outset. In our view almost all classic statistical inference is based upon the assumption (explicit or implicit) that there exists a fixed probabilistic mechanism of data generation. Unlike classic statistical inference, this book is devoted to the statistical analysis of data about complex objects with more than one probabilistic mechanism of data generation. We think that the exis tence of more than one data generation process (DGP) is the most important characteristic of com plex systems. When the hypothesis of statistical homogeneity holds true, Le., there exists only one mechanism of data generation, all statistical inference is based upon the fundamentallaws of large numbers. However, the situation is completely different when the probabilistic law of data generation can change (in time or in the phase space). In this case all data obtained must be 'sorted' in subsamples generated by different probabilistic mechanisms. Only after such classification we can make correct inferences about all DGPs. There exists yet another type of problem for complex systems. Here it is important to detect possible (but unpredictable) changes of DGPs on-line with data collection. Since the complex system can change the probabilistic mechanism of data generation, the correct statistical analysis of such data must begin with decisions about possible changes in DGPs. Codice articolo 9789048154654
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Non-Parametric Statistical Diagnosis
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Springer, Springer Dez 2010, 2010
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ISBN 13: 9789048154654
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book has a distinct philosophy and it is appropriate to make it explicit at the outset. In our view almost all classic statistical inference is based upon the assumption (explicit or implicit) that there exists a fixed probabilistic mechanism of data generation. Unlike classic statistical inference, this book is devoted to the statistical analysis of data about complex objects with more than one probabilistic mechanism of data generation. We think that the exis tence of more than one data generation process (DGP) is the most important characteristic of com plex systems. When the hypothesis of statistical homogeneity holds true, Le., there exists only one mechanism of data generation, all statistical inference is based upon the fundamentallaws of large numbers. However, the situation is completely different when the probabilistic law of data generation can change (in time or in the phase space). In this case all data obtained must be 'sorted' in subsamples generated by different probabilistic mechanisms. Only after such classification we can make correct inferences about all DGPs. There exists yet another type of problem for complex systems. Here it is important to detect possible (but unpredictable) changes of DGPs on-line with data collection. Since the complex system can change the probabilistic mechanism of data generation, the correct statistical analysis of such data must begin with decisions about possible changes in DGPs.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 472 pp. Englisch. Codice articolo 9789048154654
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