NLNG: A R Package for State Space Models
| dc.contributor.author | Hartling, Joey | |
| dc.contributor.copyright-release | Not Applicable | en_US |
| dc.contributor.degree | Master of Science | en_US |
| dc.contributor.department | Department of Mathematics & Statistics - Statistics Division | en_US |
| dc.contributor.ethics-approval | Not Applicable | en_US |
| dc.contributor.external-examiner | n/a | en_US |
| dc.contributor.graduate-coordinator | Dr. Joanna Flemming | en_US |
| dc.contributor.manuscripts | Not Applicable | en_US |
| dc.contributor.thesis-reader | Dr. Bruce Smith | en_US |
| dc.contributor.thesis-reader | Dr. David Hamilton | en_US |
| dc.contributor.thesis-supervisor | Dr. Michael Dowd | en_US |
| dc.date.accessioned | 2019-08-30T14:27:10Z | |
| dc.date.available | 2019-08-30T14:27:10Z | |
| dc.date.defence | 2019-08-27 | |
| dc.date.issued | 2019-08-30T14:27:10Z | |
| dc.description.abstract | State space models require the ability to perform filtering, smoothing and prediction during analysis. To perform these procedures fairly complex computational algorithms are required. There is a consequent need for software tools to facilitate the implementation of state space models. One of leading choices for computation data analysis is the statistical programming language, R. And a key area where analysis tools are lacking is for advanced state space models. This thesis outlines the development of a comprehensive toolkit for nonlinear and non-Gaussian state space models: the nLnG package. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10222/76368 | |
| dc.language.iso | en | en_US |
| dc.subject | state space models | en_US |
| dc.subject | time series analysis | en_US |
| dc.subject | filtering | en_US |
| dc.subject | smoothing | en_US |
| dc.subject | parameter estimation | en_US |
| dc.subject | Kalman filter | en_US |
| dc.subject | Kalman smoother | en_US |
| dc.subject | particle filter | en_US |
| dc.subject | particle smoother | en_US |
| dc.subject | state augmentation | en_US |
| dc.subject | multiple iterative filtering | en_US |
| dc.subject | random walk model | en_US |
| dc.subject | package development | en_US |
| dc.subject | R | en_US |
| dc.subject | programming | en_US |
| dc.subject | animal tracking data | en_US |
| dc.subject | nonlinear | en_US |
| dc.subject | non-Gaussian | en_US |
| dc.subject | algorithms | en_US |
| dc.title | NLNG: A R Package for State Space Models | en_US |