Hierarchical Bayesian mixture models for spatiotemporal data with nonstandard features

Research output: ThesisDoctoral Thesis

223 Downloads (Pure)

Abstract

This thesis addresses the development of Bayesian methods for the joint analysis of spatial panel data with nonstandard features. Nonstandardness comes in many forms, including nonstationarity, mixed discrete-continuous observations, and heavy tails. Mixture models provide a flexible statistical framework for handling these features, and in this thesis we develop hierarchical mixtures for nonstandard spatial panel data. We develop Markov chain Monte Carlo algorithms for these models capable of scaling to settings of high dimensionality, both in the data and the model parameters. Applications are described to simulated data, disease incidence counts, and the climate analysis of Australian rainfall data.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • The University of Western Australia
Supervisors/Advisors
  • Cripps, Edward, Supervisor
  • Lau, John, Supervisor
  • Hodkiewicz, Melinda, Supervisor
Thesis sponsors
Award date15 May 2020
DOIs
Publication statusUnpublished - 2019

Fingerprint

Dive into the research topics of 'Hierarchical Bayesian mixture models for spatiotemporal data with nonstandard features'. Together they form a unique fingerprint.

Cite this