Stochastic differential equations with R school

At the Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, 21st-24th March 2023 we will be hosting a school concerning stochastic differential equations and the YUIMA R package (Simulation and Inference for SDEs and Other Stochastic Processes, ) . The lectures will be given by members of the YUIMA team. Below is a nearly final program of the school. The dates are fixed. A similar event took place in summer 2019:

Please feel free to spread information about the school around. If anyone would be interested in coming, they should e-mail me directly. The registration deadline is 28th February 2023. However, we might end registration early if the amount of interested participats exceeds our capacity.

Students who choose to take the school as a course, can obtain 3credits for it. Examination will be through a hand-in assignment. Please let me know if you would be interested in this option.

Tentative program, can be subject to change but dates 21 March-24 March fixed

We assume the participants are familiar with R. Laboratory means that this session will contain short exercises (ca 15min) to be done by the participants on their own laptops. The YUIMA Conference sessions will be more advanced research oriented talks, everyone is welcome to attend.

DAY 1 (Mar 21)
10:45-11:30 Introduction to stochastic calculus I (stochastic processes, Brownian motion, labo with R)
13:00-14:15 Introduction to stochastic calculus II (stochastic integral and SDE, labo with R)
14:25-14:50 What can we do with YUIMA?
15:00-16:00 Linkoping Statistics Seminar + YUIMA Conference

DAY 2 (Mar 22)
09:00-10:15 Simulation of diffusion processes I (Euler-Maruyama approximation and introduction to YUIMA: yuima object, simulation, plot, Black-Scholes model, labo)
10:30-11:30 Simulation of diffusion processes II (simulation of various models for illustrations)
13:00-14:00 Laboratory (simulation with YUIMA)
14:15-15:30 Poisson process and Compound Poisson processes (introduction, simulation, laboratorory)
15:50-16:50 YUIMA Conference

DAY 3 (Mar 23)
09:00-10:15 Inference for diffusion processes I (QMLE)
10:30-11:30 Inference for diffusion processes II (quasi-Bayes estimation)
13:00-14:45 Model selection for diffusion processes
15:00-16:00 YUIMA GUI
16:10-17:10 YUIMA Conference

DAY 4 (Mar 24)
09:00-10:00 Levy processes and Levy driven SDE I (theoretical background and some examples)
10:30-11:30 Levy processes and Levy driven SDE II (simulation in YUIMA)
13:00-16:00 YUIMA Conference

Best wishes
Krzysztof Bartoszek, PhD
Docent, Senior Lecturer in Statistics
Division of Statistics and Machine Learning
Department of Computer and Information Science
Linköping University

Course Data
Type of schedule: 
Travel friendly schedule
Credits (ECTS):