Classification methods for providing personalised and class decisions. This project provides a novel approach to the clustering of multivariate samples on entities in a class that automatically matches the sample clusters across the entities, allowing for inter-sample variation between the samples in a class. The project aims to develop a widely applicable, mixture-model-based framework for the simultaneous clustering of multivariate samples with inter-sample variation in a class and for the mat ....Classification methods for providing personalised and class decisions. This project provides a novel approach to the clustering of multivariate samples on entities in a class that automatically matches the sample clusters across the entities, allowing for inter-sample variation between the samples in a class. The project aims to develop a widely applicable, mixture-model-based framework for the simultaneous clustering of multivariate samples with inter-sample variation in a class and for the matching of the clusters across the entities in the class. The project will use a statistical approach to automatically match the clusters, since the overall mixture model provides a template for the class. It will provide a basis for discriminating between different classes in addition to the identification of atypical data points within a sample and of anomalous samples within a class. Key applications include biological image analysis and the analysis of data in flow cytometry which is one of the fundamental research tools for the life scientist.Read moreRead less
Threshold models in micro-econometrics with applications to empirical models of health. The aim of this project is to develop and apply new statistical approaches to endogenously identify non-linear relationships between explanatory variable(s) and the response variable in non-linear econometric models and to illustrate these with applications important to empirical health economics. Literature proliferates in linear models with non-linear effects, but in health economics non-linear models domin ....Threshold models in micro-econometrics with applications to empirical models of health. The aim of this project is to develop and apply new statistical approaches to endogenously identify non-linear relationships between explanatory variable(s) and the response variable in non-linear econometric models and to illustrate these with applications important to empirical health economics. Literature proliferates in linear models with non-linear effects, but in health economics non-linear models dominate. This project will generalise these techniques to allow for various forms of the threshold variable(s), including categorical and continuous, endogenous and exogenous, and those measured with error.Read moreRead less
Coupling tropical cyclone and climate physics with ocean waves. It is argued that without accounting for the wave effects directly, the physics of large-scale air-sea interactions is inaccurate and incomplete. The project will introduce explicit coupling of large-scale atmospheric and oceanic phenomena with the physics of surface waves which should lead to improved predictions of tropical cyclones and climate.
Modelling Income Distributions over Space and Time: 1985-2010. The aim of this project is to develop and use interpolation and extrapolation methods, designed to overcome data scarcity, to estimate annual income distributions for countries, regions and the world for the period 1985 to 2010, facilitating measurement and comparison of changes in inequality, per capita income, poverty, and pro-poor growth, at national, regional and global levels. Reliable estimates of these welfare measures provide ....Modelling Income Distributions over Space and Time: 1985-2010. The aim of this project is to develop and use interpolation and extrapolation methods, designed to overcome data scarcity, to estimate annual income distributions for countries, regions and the world for the period 1985 to 2010, facilitating measurement and comparison of changes in inequality, per capita income, poverty, and pro-poor growth, at national, regional and global levels. Reliable estimates of these welfare measures provide valuable information for policy advisors and other researchers interested in growth and welfare of society.Read moreRead less
Stochastic majorization--minimization algorithms for data science. The changing nature of acquisition and storage data has made the process of drawing inference infeasible with traditional statistical and machine learning methods. Modern data are often acquired in real time, in an incremental nature, and are often available in too large a volume to process on conventional machinery. The project proposes to study the family of stochastic majorisation-minimisation algorithms for computation of inf ....Stochastic majorization--minimization algorithms for data science. The changing nature of acquisition and storage data has made the process of drawing inference infeasible with traditional statistical and machine learning methods. Modern data are often acquired in real time, in an incremental nature, and are often available in too large a volume to process on conventional machinery. The project proposes to study the family of stochastic majorisation-minimisation algorithms for computation of inferential quantities in an incremental manner. The proposed stochastic algorithms encompass and extend upon a wide variety of current algorithmic frameworks for fitting statistical and machine learning models, and can be used to produce feasible and practical algorithms for complex models, both current and future.
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Measuring the Commercial Real Estate Sector in Australia. This project aims to address a significant gap in our understanding of the Australian commercial real estate sector. It will use detailed data to develop sophisticated models of the prices of commercial buildings. Expected outcomes include a suite of commercial real estate price indexes for Australia, by region and property type, and a comprehensive and transparent examination of the methods used to construct them. This will shed light on ....Measuring the Commercial Real Estate Sector in Australia. This project aims to address a significant gap in our understanding of the Australian commercial real estate sector. It will use detailed data to develop sophisticated models of the prices of commercial buildings. Expected outcomes include a suite of commercial real estate price indexes for Australia, by region and property type, and a comprehensive and transparent examination of the methods used to construct them. This will shed light on a hitherto poorly measured sector and provide significant benefits by better informing market participants, guiding statistical agencies in developing such measures and better-enabling policymakers, banks, superfunds and macroprudential authorities to understand the risk profile of the sector.Read moreRead less
New Directions in Bayesian Statistics: formulation, computation and application to exemplar challenges. Bayesian statistics is a fundamental statistical and machine learning approach for density estimation, data analysis and inference. However, there remain open questions regarding the formulation of the model, the likelihood and priors, and efficient computation. This project proposes new approaches that address these issues, and applies them to two exemplar challenges: the impact of climate ch ....New Directions in Bayesian Statistics: formulation, computation and application to exemplar challenges. Bayesian statistics is a fundamental statistical and machine learning approach for density estimation, data analysis and inference. However, there remain open questions regarding the formulation of the model, the likelihood and priors, and efficient computation. This project proposes new approaches that address these issues, and applies them to two exemplar challenges: the impact of climate change on the Great Barrier Reef and better understanding neurological diseases related aging, in particular Parkinson's Disease. Read moreRead less
Identification of causal variants for complex traits. The aim of this project is to identify causal variants for complex traits in cattle and humans. Although most important traits in agriculture, medicine and evolution are complex traits, very few of the genetic variants affecting these traits are known and this undermines our understanding of how genetic variants affect a trait and practical uses of this knowledge. Huge datasets of individuals with genome sequence and phenotypes and new statis ....Identification of causal variants for complex traits. The aim of this project is to identify causal variants for complex traits in cattle and humans. Although most important traits in agriculture, medicine and evolution are complex traits, very few of the genetic variants affecting these traits are known and this undermines our understanding of how genetic variants affect a trait and practical uses of this knowledge. Huge datasets of individuals with genome sequence and phenotypes and new statistical methods provide the opportunity to close this gap. The outcome will be identification of many genomic variants causing variation in complex traits. This will benefit scientific understanding of complex traits and the ability to predict traits for individuals from their genome sequence.Read moreRead less
Towards a climate theory of tropical cyclone formation. In Earth's current climate, about 80 to 90 tropical cyclones form every year around the globe, but the reasons why cyclones form at this rate are unknown. This project will use a combination of theoretical techniques and numerical simulation to elucidate the links between large-scale climate and the rate of tropical cyclone formation. A series of climate model experiments will be performed that also have the potential to improve confidence ....Towards a climate theory of tropical cyclone formation. In Earth's current climate, about 80 to 90 tropical cyclones form every year around the globe, but the reasons why cyclones form at this rate are unknown. This project will use a combination of theoretical techniques and numerical simulation to elucidate the links between large-scale climate and the rate of tropical cyclone formation. A series of climate model experiments will be performed that also have the potential to improve confidence in our predictions of tropical cyclone incidence in a future, changed climate.Read moreRead less
Southern Ocean aerosols: sources, sinks and impact on cloud properties. This project aims to provide fundamental process-level understanding of atmospheric aerosol processes over the Southern Ocean, a region that has a profound influence on the Australian and global climate and where climate models perform poorly. Comprehensive observations during 3 Southern Ocean voyages and land-based measurements will enhance our knowledge of aerosols and cloud formation in that region and provide much-needed ....Southern Ocean aerosols: sources, sinks and impact on cloud properties. This project aims to provide fundamental process-level understanding of atmospheric aerosol processes over the Southern Ocean, a region that has a profound influence on the Australian and global climate and where climate models perform poorly. Comprehensive observations during 3 Southern Ocean voyages and land-based measurements will enhance our knowledge of aerosols and cloud formation in that region and provide much-needed data for improving global climate models. Expected outcomes include more accurate seasonal and latitudinal representations of Southern Ocean aerosol populations, properties and sources. The main benefit includes improvements in weather forecasting and future climate projection for Australia and the Southern Hemisphere.Read moreRead less