Parameter Estimation In Stochastic Differential Equations PDF Download
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| Author | : Jaya P. N. Bishwal |
| Publisher | : Springer |
| Total Pages | : 268 |
| Release | : 2007-09-26 |
| Genre | : Mathematics |
| ISBN | : 3540744487 |
Download Parameter Estimation in Stochastic Differential Equations Book in PDF, ePub and Kindle
Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modeling complex phenomena. The subject has attracted researchers from several areas of mathematics. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods.
| Author | : Simo Särkkä |
| Publisher | : Cambridge University Press |
| Total Pages | : 327 |
| Release | : 2019-05-02 |
| Genre | : Business & Economics |
| ISBN | : 1316510085 |
Download Applied Stochastic Differential Equations Book in PDF, ePub and Kindle
With this hands-on introduction readers will learn what SDEs are all about and how they should use them in practice.
| Author | : Jaya P. N. Bishwal |
| Publisher | : Springer Nature |
| Total Pages | : 634 |
| Release | : 2022-08-06 |
| Genre | : Mathematics |
| ISBN | : 3031038614 |
Download Parameter Estimation in Stochastic Volatility Models Book in PDF, ePub and Kindle
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.
| Author | : Kęstutis Kubilius |
| Publisher | : Springer |
| Total Pages | : 390 |
| Release | : 2018-01-04 |
| Genre | : Mathematics |
| ISBN | : 3319710303 |
Download Parameter Estimation in Fractional Diffusion Models Book in PDF, ePub and Kindle
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.
| Author | : Rong SITU |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 444 |
| Release | : 2006-05-06 |
| Genre | : Technology & Engineering |
| ISBN | : 0387251758 |
Download Theory of Stochastic Differential Equations with Jumps and Applications Book in PDF, ePub and Kindle
Stochastic differential equations (SDEs) are a powerful tool in science, mathematics, economics and finance. This book will help the reader to master the basic theory and learn some applications of SDEs. In particular, the reader will be provided with the backward SDE technique for use in research when considering financial problems in the market, and with the reflecting SDE technique to enable study of optimal stochastic population control problems. These two techniques are powerful and efficient, and can also be applied to research in many other problems in nature, science and elsewhere.
| Author | : Raphael Abel Kasonga |
| Publisher | : National Library of Canada |
| Total Pages | : 190 |
| Release | : 1986 |
| Genre | : |
| ISBN | : |
Download Asymptotic Parameter Estimation Theory for Stochastic Differential Equations [microform] Book in PDF, ePub and Kindle
| Author | : Yu. A. Kutoyants |
| Publisher | : |
| Total Pages | : 224 |
| Release | : 1984 |
| Genre | : Parameter estimation |
| ISBN | : |
Download Parameter Estimation for Stochastic Processes Book in PDF, ePub and Kindle
| Author | : Marc Lavielle |
| Publisher | : CRC Press |
| Total Pages | : 380 |
| Release | : 2014-07-14 |
| Genre | : Mathematics |
| ISBN | : 1482226510 |
Download Mixed Effects Models for the Population Approach Book in PDF, ePub and Kindle
Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects ModelsMixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools presents a rigorous framework for describing, implementing, and using mixed effects models. With these models, readers can perform parameter estimation and modeling across a whol
| Author | : Stefano M. Iacus |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 298 |
| Release | : 2009-04-27 |
| Genre | : Computers |
| ISBN | : 0387758399 |
Download Simulation and Inference for Stochastic Differential Equations Book in PDF, ePub and Kindle
This book covers a highly relevant and timely topic that is of wide interest, especially in finance, engineering and computational biology. The introductory material on simulation and stochastic differential equation is very accessible and will prove popular with many readers. While there are several recent texts available that cover stochastic differential equations, the concentration here on inference makes this book stand out. No other direct competitors are known to date. With an emphasis on the practical implementation of the simulation and estimation methods presented, the text will be useful to practitioners and students with minimal mathematical background. What’s more, because of the many R programs, the information here is appropriate for many mathematically well educated practitioners, too.
| Author | : Anthony Almudevar |
| Publisher | : Springer |
| Total Pages | : 354 |
| Release | : 2021-03-12 |
| Genre | : Medical |
| ISBN | : 9783030346775 |
Download Statistical Modeling for Biological Systems Book in PDF, ePub and Kindle
This book commemorates the scientific contributions of distinguished statistician, Andrei Yakovlev. It reflects upon Dr. Yakovlev’s many research interests including stochastic modeling and the analysis of micro-array data, and throughout the book it emphasizes applications of the theory in biology, medicine and public health. The contributions to this volume are divided into two parts. Part A consists of original research articles, which can be roughly grouped into four thematic areas: (i) branching processes, especially as models for cell kinetics, (ii) multiple testing issues as they arise in the analysis of biologic data, (iii) applications of mathematical models and of new inferential techniques in epidemiology, and (iv) contributions to statistical methodology, with an emphasis on the modeling and analysis of survival time data. Part B consists of methodological research reported as a short communication, ending with some personal reflections on research fields associated with Andrei and on his approach to science. The Appendix contains an abbreviated vitae and a list of Andrei’s publications, complete as far as we know. The contributions in this book are written by Dr. Yakovlev’s collaborators and notable statisticians including former presidents of the Institute of Mathematical Statistics and of the Statistics Section of the AAAS. Dr. Yakovlev’s research appeared in four books and almost 200 scientific papers, in mathematics, statistics, biomathematics and biology journals. Ultimately this book offers a tribute to Dr. Yakovlev’s work and recognizes the legacy of his contributions in the biostatistics community.
