Simulation and Inference for Stochastic Processes with YUIMA: A Comprehensive R Framework for SDEs and Other Stochastic Processes

by Stefano M. Iacus, Nakahiro Yoshida

Overview

The YUIMA package is the first comprehensive R framework based on S4 classes and methods which allows for the simulation of shastic differential equations driven by Wiener process, Lévy processes or fractional Brownian motion, as well as CARMA, COGARCH, and Point processes. The package performs various central statistical analyses such as quasi maximum likelihood estimation, adaptive Bayes estimation, structural change point analysis, hypotheses testing, asynchronous covariance estimation, lead-lag estimation, LASSO model selection, and so on. YUIMA also supports shastic numerical analysis by fast computation of the expected value of functionals of shastic processes through automatic asymptotic expansion by means of the Malliavin calculus. All models can be multidimensional, multiparametric or non parametric.The book explains briefly the underlying theory for simulation and inference of several classes of shastic processes and then presents both simulation experiments and applications to real data. Although these processes have been originally proposed in physics and more recently in finance, they are becoming popular also in biology due to the fact the time course experimental data are now available. The YUIMA package, available on CRAN, can be freely downloaded and this companion book will make the user able to start his or her analysis from the first page.

Product Details

ISBN-13:	9783319555676
Publisher:	Springer International Publishing
Publication date:	06/02/2018
Series:	Use R!
Edition description:	1st ed. 2018
Pages:	268
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

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 shastic 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.

Table of Contents

1 Introduction.- 2 Diffusion processes.- 3 Compound Poisson processes.- 4 Shastic differential equations driven by Lévy processes.- 5 Shastic differential equations driven by the fractional Brownian motion.- 6 CARMA models.- 7 COGARCH models.- Reference.- Index.

Editorial Reviews

The book is written in an accessible style … . a wide audience can benefit for a through overview of the topic and the R package.” (Irina Ioana Mohorianu, zbMATH 1458.60004, 2021)

From the Publisher