- Išsiųsime per 10–14 d.d.
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- Autorius: David Ruppert
- Leidėjas: Springer
- ISBN-10: 1493926136
- ISBN-13: 9781493926138
- Formatas: 15.6 x 23.4 x 4 cm, kieti viršeliai
- Kalba: Anglų
- Extra -15 % nuolaida šiai knygai su kodu: ENG15
Statistics and Data Analysis for Financial Engineering + nemokamas atvežimas! | knygos.lt
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Aprašymas
The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest.
EXTRA 15 % nuolaida su kodu: ENG15
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Nuolaidos kodas galioja perkant nuo 10 €. Nuolaidos nesumuojamos.
- Autorius: David Ruppert
- Leidėjas: Springer
- ISBN-10: 1493926136
- ISBN-13: 9781493926138
- Formatas: 15.6 x 23.4 x 4 cm, kieti viršeliai
- Kalba: Anglų
The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest.
Atsiliepimai