Multivariate Modelling of Irregularly Spaced Financial Transduction Data PDF Download

Author: Ingmar Nolte
Publisher:
ISBN:
Category :
Languages : en
Pages : 218

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Author: Nikolaus Hautsch
Publisher: Springer Science & Business Media
ISBN: 3642170153
Category : Business & Economics
Languages : en
Pages : 297

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Book Description
This book provides a methodological framework to model univariate and multivariate irregularly spaced financial data. It gives a thorough review of recent developments in the econometric literature, puts forward existing approaches and opens up new directions. The book presents alternative ways to model so-called financial point processes using dynamic duration as well as intensity models and discusses their ability to account for specific features of point process data, like the occurrence of time-varying covariates, censoring mechanisms and multivariate structures. Moreover, it illustrates the use of various types of financial point processes to model financial market activity from different viewpoints and to construct volatility and liquidity measures under explicit consideration of the passing trading time.

Author: Nikolaus Hautsch
Publisher:
ISBN: 9783642170164
Category :
Languages : en
Pages : 304

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Author: Ghysels, Eric
Publisher: Montréal : CIRANO
ISBN:
Category :
Languages : en
Pages : 26

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Author: Christopher John Ulph
Publisher:
ISBN:
Category : Financial institutions
Languages : en
Pages : 259

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Author: Robert B. Gramacy
Publisher: CRC Press
ISBN: 1000766209
Category : Mathematics
Languages : en
Pages : 560

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Book Description
Computer simulation experiments are essential to modern scientific discovery, whether that be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are meta-models of computer simulations, used to solve mathematical models that are too intricate to be worked by hand. Gaussian process (GP) regression is a supremely flexible tool for the analysis of computer simulation experiments. This book presents an applied introduction to GP regression for modelling and optimization of computer simulation experiments. Features: • Emphasis on methods, applications, and reproducibility. • R code is integrated throughout for application of the methods. • Includes more than 200 full colour figures. • Includes many exercises to supplement understanding, with separate solutions available from the author. • Supported by a website with full code available to reproduce all methods and examples. The book is primarily designed as a textbook for postgraduate students studying GP regression from mathematics, statistics, computer science, and engineering. Given the breadth of examples, it could also be used by researchers from these fields, as well as from economics, life science, social science, etc.

Author: Olivier Pourret
Publisher: John Wiley & Sons
ISBN: 9780470994542
Category : Mathematics
Languages : en
Pages : 446

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Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks. The book: Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model. Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations. Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees. Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user. Offers a historical perspective on the subject and analyses future directions for research. Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.

Author: Jancy C. McPhee
Publisher: U. S. National Aeronautics & Space Administration
ISBN:
Category : Biography & Autobiography
Languages : en
Pages : 396

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Author: H. L. McMurry
Publisher:
ISBN:
Category : Nuclear reactors
Languages : en
Pages : 24

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Author: Pengcheng Fu
Publisher: John Wiley & Sons
ISBN: 9780470437971
Category : Technology & Engineering
Languages : en
Pages : 672

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Book Description
The genomic revolution has opened up systematic investigations and engineering designs for various life forms. Systems biology and synthetic biology are emerging as two complementary approaches, which embody the breakthrough in biology and invite application of engineering principles. Systems Biology and Synthetic Biology emphasizes the similarity between biology and engineering at the system level, which is important for applying systems and engineering theories to biology problems. This book demonstrates to students, researchers, and industry that systems biology relies on synthetic biology technologies to study biological systems, while synthetic biology depends on knowledge obtained from systems biology approaches.