Continuous Parameter Markov Processes and Stochastic Differential Equations, Bhattacharya

Автор: Hans Georg Bock; Thomas Carraro; Willi J?ger; Stef
Название: Model Based Parameter Estimation
ISBN: 3642440762 ISBN-13(EAN): 9783642440762
Издательство: Springer
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Цена: 121110.00 T
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Описание: This book features papers from a workshop on parameter estimation held in 2009 in Heidelberg. It combines mathematical and numerical methods to apply parameter estimation and optimum experimental design in a range of contexts.

Автор: Shuping Chen; Xunjing Li; Jiongming Yong; Xun Yu Z
Название: Control of Distributed Parameter and Stochastic Systems
ISBN: 147574868X ISBN-13(EAN): 9781475748680
Издательство: Springer
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Цена: 139750.00 T
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Описание: Proceedings of the IFIP WG 7.2 International Conference, June 19-22, 1998, Hangzhou, PR of China

Автор: Bishwal
Название: Parameter Estimation in Stochastic Volatility Models
ISBN: 3031038630 ISBN-13(EAN): 9783031038631
Издательство: Springer
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Цена: 139750.00 T
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Описание: This book develops alternative methods to estimate the unknown parameters in stochastic volatility models, offering a new approach to test model accuracy. While there is ample research to document stochastic differential equation models driven by Brownian motion based on discrete observations of the underlying diffusion process, these traditional methods often fail to estimate the unknown parameters in the unobserved volatility processes. This text studies the second order rate of weak convergence to normality to obtain refined inference results like confidence interval, as well as nontraditional continuous time stochastic volatility models driven by fractional Levy processes. By incorporating jumps and long memory into the volatility process, these new methods will help better predict option pricing and stock market crash risk. Some simulation algorithms for numerical experiments are provided.

Автор: Bishwal
Название: Parameter Estimation in Stochastic Volatility Models
ISBN: 3031038606 ISBN-13(EAN): 9783031038600
Издательство: Springer
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Цена: 139750.00 T
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Описание: This book develops alternative methods to estimate the unknown parameters in stochastic volatility models, offering a new approach to test model accuracy.

Автор: Tiberiu Colosi; Mihail-Ioan Abrudean; Mihaela-Ligi
Название: Numerical Simulation of Distributed Parameter Processes
ISBN: 3319000136 ISBN-13(EAN): 9783319000138
Издательство: Springer
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Цена: 139750.00 T
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Описание: Covering processes formed by systems of partial derivative equations (PDEs), including systems of ordinary differential equations (ODEs), this book interprets and uses the matrix of partial derivatives of the state vector with applications to engineering.

Автор: Tiberiu Colosi; Mihail-Ioan Abrudean; Mihaela-Ligi
Название: Numerical Simulation of Distributed Parameter Processes
ISBN: 3319033263 ISBN-13(EAN): 9783319033266
Издательство: Springer
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Цена: 139310.00 T
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Описание: Covering processes formed by systems of partial derivative equations (PDEs), including systems of ordinary differential equations (ODEs), this book interprets and uses the matrix of partial derivatives of the state vector with applications to engineering.

Автор: Lakshmikantham, V.
Название: Method of Variation of Parameters for Dynamic Systems
ISBN: 0367455773 ISBN-13(EAN): 9780367455774
Издательство: Taylor&Francis
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Цена: 65320.00 T
Наличие на складе: Невозможна поставка.
Описание: Method of Variation of Parameters for Dynamic Systems presents a systematic and unified theory of the development of the theory of the method of variation of parameters, its unification with Lyapunov`s method and typical applications of these methods.

Автор: Bernd Hofmann; Antonio Leit?o; Jorge P. Zubelli
Название: New Trends in Parameter Identification for Mathematical Models
ISBN: 3319889982 ISBN-13(EAN): 9783319889986
Издательство: Springer
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Цена: 111790.00 T
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Описание: The Proceedings volume contains 16 contributions to the IMPA conference “New Trends in Parameter Identification for Mathematical Models”, Rio de Janeiro, Oct 30 – Nov 3, 2017, integrating the “Chemnitz Symposium on Inverse Problems on Tour”.  This conference is part of the “Thematic Program on Parameter Identification in Mathematical Models” organized  at IMPA in October and November 2017. One goal is to foster the scientific collaboration between mathematicians and engineers from the Brazialian, European and Asian communities. Main topics are iterative and variational regularization methods in Hilbert and Banach spaces for the stable approximate solution of ill-posed inverse problems, novel methods for parameter identification in partial differential equations, problems of tomography ,  solution of coupled conduction-radiation problems at high temperatures, and the statistical solution of inverse problems with applications in physics.

Автор: Chen, Han-Fu
Название: Recursive Identification and Parameter Estimation
ISBN: 1466568844 ISBN-13(EAN): 9781466568846
Издательство: Taylor&Francis
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Цена: 148010.00 T
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Автор: Karl-Rudolf Koch
Название: Parameter Estimation and Hypothesis Testing in Linear Models
ISBN: 3642084613 ISBN-13(EAN): 9783642084614
Издательство: Springer
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Цена: 97780.00 T
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Описание: Readers will find here presentations of the Gauss-Markoff model, the analysis of variance, the multivariate model, the model with unknown variance and covariance components and the regression model as well as the mixed model for estimating random parameters.

Автор: Kubilius Kęstutis, Mishura Yuliya, Ralchenko Kostiantyn
Название: Parameter Estimation in Fractional Diffusion Models
ISBN: 331989031X ISBN-13(EAN): 9783319890319
Издательство: Springer
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Цена: 111790.00 T
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Описание: This book is devoted to parameter estimation in diffusion models involving fractional Brownian motion and related processes. In particular, models of financial markets demonstrate various kinds of memory and usually this memory is modeled by fractional Brownian diffusion.

Автор: Seber George A. F., Schofield Matthew R.
Название: Capture-Recapture: Parameter Estimation for Open Animal Populations
ISBN: 3030181898 ISBN-13(EAN): 9783030181895
Издательство: Springer
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Цена: 83850.00 T
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Описание: This comprehensive book, rich with applications, offers a quantitative framework for the analysis of the various capture-recapture models for open animal populations, while also addressing associated computational methods. The state of our wildlife populations provides a litmus test for the state of our environment, especially in light of global warming and the increasing pollution of our land, seas, and air. In addition to monitoring our food resources such as fisheries, we need to protect endangered species from the effects of human activities (e.g. rhinos, whales, or encroachments on the habitat of orangutans). Pests must be be controlled, whether insects or viruses, and we need to cope with growing feral populations such as opossums, rabbits, and pigs. Accordingly, we need to obtain information about a given population’s dynamics, concerning e.g. mortality, birth, growth, breeding, sex, and migration, and determine whether the respective population is increasing , static, or declining. There are many methods for obtaining population information, but the most useful (and most work-intensive) is generically known as “capture-recapture,” where we mark or tag a representative sample of individuals from the population and follow that sample over time using recaptures, resightings, or dead recoveries. Marks can be natural, such as stripes, fin profiles, and even DNA; or artificial, such as spots on insects. Attached tags can, for example, be simple bands or streamers, or more sophisticated variants such as radio and sonic transmitters. To estimate population parameters, sophisticated and complex mathematical models have been devised on the basis of recapture information and computer packages. This book addresses the analysis of such models. It is primarily intended for ecologists and wildlife managers who wish to apply the methods to the types of problems discussed above, though it will also benefit researchers and graduate students in ecology. Familiarity with basic statistical concepts is essential.