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Author: Renat Khabibullin
Publisher: LAP Lambert Academic Publishing
ISBN: 9783845423050
Category :
Languages : en
Pages : 68
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Book Description
The problem of dynamic joint distribution estimation is very important from both theoretical and practical points of view: econometricians would be interested in developing new techniques and approaches to model dynamic joint distributions, whereas practitioners (especially, risk and asset managers) would be interested in obtaining dynamic distributions for computing risk measures and making optimal portfolio choices. This book introduces a new sequential methodology for dynamic joint distributions modeling based on combining small-dimensional distributions into higher-dimensional ones. The new proposition uses marginal and bivariate distributions as inputs, combines them to capture the dependence between one marginal and one bivariate, and then aggregates all of the dependencies to obtain trivariate distributions. Higher-dimensional distributions are built in a similar manner from one-dimension-smaller distributions and univariate ones through compounding and then aggregating them into a single distribution. Additionally, the book demonstrates how to apply this new sequential technique to model five-dimensional distribution of DJIA constituents.
Author: Renat Khabibullin
Publisher: LAP Lambert Academic Publishing
ISBN: 9783845423050
Category :
Languages : en
Pages : 68
Get Book Here
Book Description
The problem of dynamic joint distribution estimation is very important from both theoretical and practical points of view: econometricians would be interested in developing new techniques and approaches to model dynamic joint distributions, whereas practitioners (especially, risk and asset managers) would be interested in obtaining dynamic distributions for computing risk measures and making optimal portfolio choices. This book introduces a new sequential methodology for dynamic joint distributions modeling based on combining small-dimensional distributions into higher-dimensional ones. The new proposition uses marginal and bivariate distributions as inputs, combines them to capture the dependence between one marginal and one bivariate, and then aggregates all of the dependencies to obtain trivariate distributions. Higher-dimensional distributions are built in a similar manner from one-dimension-smaller distributions and univariate ones through compounding and then aggregating them into a single distribution. Additionally, the book demonstrates how to apply this new sequential technique to model five-dimensional distribution of DJIA constituents.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 276
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Book Description
Author: Mark E. Johnson
Publisher: John Wiley & Sons
ISBN: 1118150732
Category : Mathematics
Languages : en
Pages : 248
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Book Description
Provides state-of-the-art coverage for the researcher confronted with designing and executing a simulation study using continuous multivariate distributions. Concise writing style makes the book accessible to a wide audience. Well-known multivariate distributions are described, emphasizing a few representative cases from each distribution. Coverage includes Pearson Types II and VII elliptically contoured distributions, Khintchine distributions, and the unifying class for the Burr, Pareto, and logistic distributions. Extensively illustrated--the figures are unique, attractive, and reveal very nicely what distributions ``look like.'' Contains an extensive and up-to-date bibliography culled from journals in statistics, operations research, mathematics, and computer science.
Author: Mike West
Publisher: Springer Science & Business Media
ISBN: 1475793650
Category : Mathematics
Languages : en
Pages : 720
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Book Description
In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.
Author: Samuel Kotz
Publisher: John Wiley & Sons
ISBN: 0471183873
Category : Mathematics
Languages : en
Pages : 752
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Book Description
Seit dem Erscheinen der ersten Auflage dieses Werkes (1972) hat sich das Gebiet der kontinuierlichen multivariaten Verteilungen rasch weiterentwickelt. Moderne Anwendungsfelder sind die Erforschung von Hochwasser, Erdbeben, Regenfällen und Stürmen. Entsprechend wurde das Buch überarbeitet und erweitert: Nunmehr zwei Bände beschreiben eine Vielzahl multivariater Verteilungsmodelle anhand zahlreicher Beispiele. (05/00)
Author: Harry Joe
Publisher: CRC Press
ISBN: 9780412073311
Category : Mathematics
Languages : en
Pages : 422
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Book Description
This book on multivariate models, statistical inference, and data analysis contains deep coverage of multivariate non-normal distributions for modeling of binary, count, ordinal, and extreme value response data. It is virtually self-contained, and includes many exercises and unsolved problems.
Author: Giovanni Petris
Publisher: Springer Science & Business Media
ISBN: 0387772383
Category : Mathematics
Languages : en
Pages : 258
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Book Description
State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.
Author: Konstantinos Fokianos
Publisher: CRC Press
ISBN: 9781482248050
Category :
Languages : en
Pages : 220
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Book Description
Author: Tevfik Aktekin
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
In this article, we propose a class of multivariate non-Gaussian time series models which include dynamic versions of many well-known distributions and consider their Bayesian analysis. A key feature of our proposed model is its ability to account for correlations across time as well as across series (contemporary) via a common random environment. The proposed modeling approach yields analytically tractable dynamic marginal likelihoods, a property not typically found outside of linear Gaussian time series models. These dynamic marginal likelihoods can be tied back to known static multivariate distributions such as the Lomax, generalized Lomax, and the multivariate Burr distributions. The availability of the marginal likelihoods allows us to develop efficient estimation methods for various settings using Markov chain Monte Carlo as well as sequential Monte Carlo methods. Our approach can be considered to be a multivariate generalization of commonly used univariate non-Gaussian class of state space models. To illustrate our methodology, we use simulated data examples and a real application of multivariate time series for modeling the joint dynamics of stochastic volatility in financial indexes, the VIX and VXN.
Author: Adrian F. M. Smith
Publisher:
ISBN:
Category : Business & Economics
Languages : en
Pages : 428
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Book Description
Throughout his career Dennis Lindley has insisted on thinking things through from first principles and on basing developments on firm, logical foundations. Although his fundamental contributions to Bayesian statistics and decision theory are universally recognised, it is less well known that he arrived at the Bayesian position as a result of seeking to establish a rigorous axiomatic justification for classical statistical procedures.
