Sprache: Englisch
Verlag: Springer International Publishing AG, 2018
ISBN 10: 3319555677 ISBN 13: 9783319555676
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gebundene Ausgabe. Zustand: Gut. 284 Seiten Der Erhaltungszustand des hier angebotenen Werks ist trotz seiner Bibliotheksnutzung sehr sauber und kann entsprechende Merkmale aufweisen (Rückenschild, Instituts-Stempel.). In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 575.
Sprache: Englisch
Verlag: Springer-Verlag New York Inc, 2018
ISBN 10: 3319555677 ISBN 13: 9783319555676
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In den WarenkorbPaperback. Zustand: Brand New. 284 pages. 9.25x6.10x0.67 inches. In Stock.
Sprache: Englisch
Verlag: Springer International Publishing, 2018
ISBN 10: 3319555677 ISBN 13: 9783319555676
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In den WarenkorbZustand: New. Stefano M. Iacus is full professor of Statistics at the University of Milan, on leave at the Joint Research Centre of the European Commission. Former R-core member (1999-2017) and R Foundation Member. Giuseppe Porro is full.
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In den WarenkorbPaperback. Zustand: Brand New. 220 pages. 9.19x6.13x0.50 inches. In Stock.
Sprache: Englisch
Verlag: Springer, Palgrave Macmillan, 2018
ISBN 10: 3319555677 ISBN 13: 9783319555676
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The YUIMA package is the first comprehensive R framework based on S4 classes and methods which allows for the simulation of stochastic differential equations driven by Wiener process, Lévy processes or fractional Brownian motion, as well as CARMA, COGARCH, and Point processes. The package performs various central statistical analyses such as quasi maximum likelihood estimation, adaptive Bayes estimation, structural change point analysis, hypotheses testing, asynchronous covariance estimation, lead-lag estimation, LASSO model selection, and so on. YUIMA also supports stochastic numerical analysis by fast computation of the expected value of functionals of stochastic processes through automatic asymptotic expansion by means of the Malliavin calculus. All models can be multidimensional, multiparametric or non parametric.The book explains briefly the underlying theory for simulation and inference of several classes of stochastic processes and then presents both simulation experiments and applications to real data. Although these processes have been originally proposed in physics and more recently in finance, they are becoming popular also in biology due to the fact the time course experimental data are now available. The YUIMA package, available on CRAN, can be freely downloaded and this companion book will make the user able to start his or her analysis from the first page.
Taschenbuch. Zustand: Neu. Simulation and Inference for Stochastic Processes with YUIMA | A Comprehensive R Framework for SDEs and Other Stochastic Processes | Stefano M. Iacus (u. a.) | Taschenbuch | Use R! | xiii | Englisch | 2018 | Springer | EAN 9783319555676 | 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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In den WarenkorbZustand: New. pp. 472.
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In den WarenkorbZustand: New. In English.
Sprache: Englisch
Verlag: Taylor & Francis Ltd Sep 2023, 2023
ISBN 10: 1032043164 ISBN 13: 9781032043166
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Taschenbuch. Zustand: Neu. Neuware - The authors describe a social media index for measuring subjective well-being, relying on the availability of big data sources provided by the social networking sites, and on one of the most recent techniques for sentiment analysis. This approach disentangles the main methodological issues raised in the literature on well-being measurement.
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Zustand: New. A practical text for calibrating financial models and numerical option pricing featuring R, Option Pricing and Estimation of Financial Models With R distills inference and simulation of stochastic process in the field of model calibration for financial times series modeled with continuous time processes and numerical option pricing. Num Pages: 472 pages, Illustrations. BIC Classification: KFFM; KJ; PB. Category: (P) Professional & Vocational. Dimension: 237 x 163 x 29. Weight in Grams: 810. . 2011. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.
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Taschenbuch. Zustand: Neu. Simulation and Inference for Stochastic Differential Equations | With R Examples | Stefano M. Iacus | Taschenbuch | xviii | Englisch | 2010 | Springer | EAN 9781441926074 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Stochastic di erential equations model stochastic evolution as time evolves. These models have a variety of applications in many disciplines and emerge naturally in the study of many phenomena. Examples of these applications are physics (see, e. g. , [176] for a review), astronomy [202], mechanics [147], economics [26], mathematical nance [115], geology [69], genetic analysis (see, e. g. , [110], [132], and [155]), ecology [111], cognitive psychology (see, e. g. , [102], and [221]), neurology [109], biology [194], biomedical sciences [20], epidemi- ogy [17], political analysis and social processes [55], and many other elds of science and engineering. Although stochastic di erential equations are quite popular models in the above-mentioned disciplines, there is a lot of mathem- ics behind them that is usually not trivial and for which details are not known to practitioners or experts of other elds. In order to make this book useful to a wider audience, we decided to keep the mathematical level of the book su ciently low and often rely on heuristic arguments to stress the underlying ideas of the concepts introduced rather than insist on technical details. Ma- ematically oriented readers may nd this approach inconvenient, but detailed references are always given in the text. As the title of the book mentions, the aim of the book is twofold.
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In den WarenkorbZustand: New. pp. 188.
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Stochastic di erential equations model stochastic evolution as time evolves. These models have a variety of applications in many disciplines and emerge naturally in the study of many phenomena. Examples of these applications are physics (see, e. g. , [176] for a review), astronomy [202], mechanics [147], economics [26], mathematical nance [115], geology [69], genetic analysis (see, e. g. , [110], [132], and [155]), ecology [111], cognitive psychology (see, e. g. , [102], and [221]), neurology [109], biology [194], biomedical sciences [20], epidemi- ogy [17], political analysis and social processes [55], and many other elds of science and engineering. Although stochastic di erential equations are quite popular models in the above-mentioned disciplines, there is a lot of mathem- ics behind them that is usually not trivial and for which details are not known to practitioners or experts of other elds. In order to make this book useful to a wider audience, we decided to keep the mathematical level of the book su ciently low and often rely on heuristic arguments to stress the underlying ideas of the concepts introduced rather than insist on technical details. Ma- ematically oriented readers may nd this approach inconvenient, but detailed references are always given in the text. As the title of the book mentions, the aim of the book is twofold.
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