| Item type | Current location | Collection | Call number | Copy number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|---|
| E-Books | Biblioteca da FCTUNL | Não Ficção | QA273.SPR FCT 97610 (Browse shelf) | 1 | Available |
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| QA273.SPR FCT 97532 Progress in high-dimensional percolation and random graphs | QA273.SPR FCT 97571 Random measures, theory and applications | QA273.SPR FCT 97580 Discrete probability models and methods | QA273.SPR FCT 97610 Parameter estimation in fractional diffusion models | QA273.SPR FCT 97628 Random ordinary differential equations and their numerical solution | QA273.SPR FCT 97629 Robust multivariate analysis | QA273.SPR FCT 97663 A forward - ackward SDEs approach to pricing in carbon markets |
Colocação: Online
This book is devoted to parameter estimation in diffusion models involving fractional Brownian motion and related processes. For many years now, standard Brownian motion has been (and still remains) a popular model of randomness used to investigate processes in the natural sciences, financial markets, and the economy. The substantial limitation in the use of stochastic diffusion models with Brownian motion is due to the fact that the motion has independent increments, and, therefore, the random noise it generates is “white,” i.e., uncorrelated. However, many processes in the natural sciences, computer networks and financial markets have long-term or short-term dependences, i.e., the correlations of random noise in these processes are non-zero, and slowly or rapidly decrease with time. In particular, models of financial markets demonstrate various kinds of memory and usually this memory is modeled by fractional Brownian diffusion. Therefore, the book constructs diffusion models with memory and provides simple and suitable parameter estimation methods in these models, making it a valuable resource for all researchers in this field. The book is addressed to specialists and researchers in the theory and statistics of stochastic processes, practitioners who apply statistical methods of parameter estimation, graduate and post-graduate students who study mathematical modeling and statistics.
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