Statistical Methods for Stochastic Differential Equations

Matthieu Kessler, Department of Applied Mathematics and Statistics, University of Cartagena, SpainAlexander Lindner, Institute of Mathematics and Statistics, TU Braunschweig, GermanyMichael Sorensen, Department of Mathematical Sciences, University of Copenhagen, Denmark

"… an excellent resource for anyone currently active in research in this area, interested in getting into research in the area, or just interested in the topic. I cannot think of another source that provides detailed yet accessible introductions of this quality and timeliness to the major issues of interest in this area. … As noted in the preface, the idea is to get young researchers ‘quickly to the forefront of knowledge and research.’ … The book succeeds in delivering on this goal. A careful reading of the chapters of this book would go a long way toward putting one in a position to begin contributing to the large and rapidly growing body of research in this important area of statistics. It would certainly be an excellent resource for teaching advanced Ph.D. courses. … This is a wonderful book for anyone interested in SDEs. I highly recommend it and am happy to have it on my bookshelf."—Garland B. Durham, Journal of the American Statistical Association, March 2014"The contributors are all renowned specialists in the field … the last four chapters are generally well written, informative, and cover a wide range of different aspects of statistics for SDE … the first three chapters … constitute an original and very useful contribution in a field that too often has the reputation of being technical and somehow austere. … I strongly recommend the book for anyone interested in the wide topic of statistical methods for SDE, whether she or he is a specialist or a student starting in the field."—Marc Hoffmann, Université Paris–Dauphine Sørensen, CHANCE, 26.3"… a good collection of useful and interesting articles … [I have] no hesitation in recommending the book."—Tusheng Zhang, Journal of Time Series Analysis, 2013

• Estimating functions for diffusion-type processes. The econometrics of high frequency data. Statistics and high frequency data. Importance sampling techniques for estimation of diffusion models. Non parametric estimation of the coefficients of ergodic diffusion processes based on high frequency data. Ornstein-Uhlenbeck related models driven by Levy processes. Parameter estimation for multiscale diffusions: an overview.