This thesis explores the methodology of state, and in particular, parameter estimation for time
series datasets. Various approaches are investigated that are suitable for nonlinear models
and non-Gaussian observations using state space models. The methodologies are applied to a
dataset consisting of the historical lynx and hare populations, typically modeled by the Lotka-
Volterra equations. With this model and the observed dataset, particle filtering and parameter
estimation methods are implemented as a way to better predict the state of the system.
Methods for parameter estimation considered include: maximum likelihood estimation, state
augmented particle filtering, multiple iterative filtering and particle Markov chain Monte
Carlo (PMCMC) methods. The specific advantages and disadvantages for each technique
are discussed. However, in most cases, PMCMC is the preferred parameter estimation
solution. It has the advantage over other approaches in that it can well approximate any
posterior distribution from which inference can be made. / Master's thesis
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:NSHD.ca#10222/14741 |
| Date | 23 April 2012 |
| Creators | Wong, Jessica |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
Page generated in 0.0022 seconds