Bayesian estimation in a bidimensional Ornstein-Uhlenbeck process

Bayesian estimation in a bidimensional Ornstein-Uhlenbeck process

van der Voort, J.C. (TU Delft Electrical Engineering, Mathematics and Computer Science)

van der Meulen, F.H. (mentor)

Delft University of Technology

Applied Mathematics

2020-08-10

The goal of this thesis is to estimate parameters in a bidimensional Ornstein-Uhlenbeck process, namely a diffusion model which can be found in Favetto and Samson (2010), which considers plasma and interstitium concentrations. We first look at a general linear stochastic differential equation and some properties. Then we simulate possible paths and observations based on the diffusion model, and derive the underlying state space model. We consider state estimation, where we use the Kalman filter and smoother algorithm. It turns out that the smoother outperforms the Kalman filter. Next, we apply and derive the Liu and West filter for state estimation. We see that the performance for the Liu and West filter is close to the Kalman filter. Furthermore, we look at the parameter estimation. We again use and derive the Liu and West filter, but now for parameter estimation. We first apply this filter to a linear AR(1) model, which gives good results. Then we start with estimating one parameter in the diffusion model and finally we estimate all 7 parameters in the model, using priors concentrated around the true value and 10.000 particles. For one parameter, we obtain good results. For all 7 parameters, results are satisfactory, with for fully observed data better results than for partially observed data. We also look at the influence of the number of particles: for 5.000 particles estimation results are somewhat worse than for 10.000 particles. Furthermore, we change some priors such that they are no longer concentrated around the true value. From this, we see that the Liu and West filter does not seem to perform as well for certain choices of priors. Next, we look at state and parameter estimation in a non-linear AR(1) model using the Liu and West filter, which gives rather good results. Finally, we apply the Liu and West filter once more, but now with a real data set. In this case we are able to obtain parameter values that provide a reasonable estimate for the sum of the concentrations, but for the interstitium concentration it leaves much uncertainty.

statistics
particle filter
Bayesian
Kalman filter
state estimation
parameter estimation

http://resolver.tudelft.nl/uuid:fb875b84-b2b2-47e8-a7cf-4068074ce907

Student theses

bachelor thesis

© 2020 J.C. van der Voort