Hierarchical Bayesian mixture models for spatiotemporal data with nonstandard features

Michael Bertolacci

doi:10.26182/5ee9601b7a2fd

Hierarchical Bayesian mixture models for spatiotemporal data with nonstandard features

Michael Bertolacci

School of Physics, Mathematics and Computing

Research output: Thesis › Doctoral Thesis

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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 language	English
Qualification	Doctor of Philosophy
Awarding Institution	The University of Western Australia
Supervisors/Advisors	Cripps, Edward, Supervisor Lau, John, Supervisor Hodkiewicz, Melinda, Supervisor
Thesis sponsors	Australian Government Research Training Program
Award date	15 May 2020
DOIs	https://doi.org/10.26182/5ee9601b7a2fd
Publication status	Unpublished - 2019

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THESIS - DOCTOR OF PHILOSOPHY - BERTOLACCI, Michael George Norman - 2019
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AB - 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.

KW - Bayesian statistics

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KW - mixture models

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KW - hierarchical models

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DO - 10.26182/5ee9601b7a2fd

M3 - Doctoral Thesis

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Hierarchical Bayesian mixture models for spatiotemporal data with nonstandard features

Abstract

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