This book covers a highly relevant topic that is of wide interest, especially in finance, engineering and computational biology. With an emphasis on the practical implementation of the simulation and estimation methods presented, the text will be useful to practitioners with minimal mathematical background.

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. Many of the methods presented in the book have, so far, not been used much in practice because of the lack of an implementation in a unified framework. Iacus' book bridges this gap. With the R code included, a lot of useful methods become easy to use.

Ready-to-use functions allow for instant analysis on real life data Many figures give immediate feeling on how methods perform Theoretical results are presented side-by-side with R code to ease the passage from theory to practice Includes supplementary material: sn.pub/extras

Autorentext

Stefano M. Iacus, PhD, is full professor of statistics in the Department of Economics, Management and Quantitative Methods at the University of Milan. He has been a member of the R Core Team (1999-2014) for the development of the R statistical environment and is now a member of the R Foundation. His research interests include inference for stochastic processes, simulation, computational statistics, causal inference, text mining, and sentiment analysis. Nakahiro Yoshida, PhD, is full professor at the Graduate School of Mathematical Sciences, University of Tokyo. He is working in theoretical statistics, probability theory, computational statistics, and financial data analysis. He was awarded the Japan Statistical Society Award in 2009 and the Analysis Prize from the Mathematical Society of Japan in 2006.

Inhalt
Stochastic Processes and Stochastic Differential Equations.- Numerical Methods for SDE.- Parametric Estimation.- Miscellaneous Topics.