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
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
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
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
Detection and Quantification of General Fetal Movements from Accelerometer Measurements using Nonstationary Signal Processing Techniques. There are approximately 1,750 fetal deaths per year in Australian with about one-third occurring late in gestation and without an apparent cause. The development of an automated system capable of long-term monitoring of fetal health will result in accurate diagnoses and prediction of future outcome. This will, in turn, allow early intervention by the clinicia ....Detection and Quantification of General Fetal Movements from Accelerometer Measurements using Nonstationary Signal Processing Techniques. There are approximately 1,750 fetal deaths per year in Australian with about one-third occurring late in gestation and without an apparent cause. The development of an automated system capable of long-term monitoring of fetal health will result in accurate diagnoses and prediction of future outcome. This will, in turn, allow early intervention by the clinician to reduce fetal deaths and enhance the chances of good outcomes with resultant savings in social and financial costs to the community. The development of such equipment would spawn future research into intervention treatments and contribute to Australia's position as a world leader in computerised health monitoring systems.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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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
New entropy measures of short term signals for smart wearable devices. This project aims to improve reliability and accuracy of wearable devices by developing a new set of computationally efficient algorithms. Wearable devices can be very effective in remote and continuous monitoring to detect short or bursty anomalous events. Present devices are unable to detect such events effectively due to limited capability in processing short length signal. This project will provide computationally efficie ....New entropy measures of short term signals for smart wearable devices. This project aims to improve reliability and accuracy of wearable devices by developing a new set of computationally efficient algorithms. Wearable devices can be very effective in remote and continuous monitoring to detect short or bursty anomalous events. Present devices are unable to detect such events effectively due to limited capability in processing short length signal. This project will provide computationally efficient algorithms for signal quality analysis and enhanced feature extraction methods in resource constrained wearable devices. This will improve the reliability and performance of wearable devices for adoption in intelligent decision-making systems.Read moreRead less
Early-Stage Medical Diagnostics by Plasmon-Mediated Gas Sensing. This project will investigate the use plasmonic absorption of light in metal nanostructures to activate the selective oxidation/reduction of a gas molecule on a semiconductor nanoparticle. This concept will be used with the aim of developing a sensing technique capable of measuring ultra-low concentrations (ppb) of breath markers for lung cancer detection. It is expected that porous sensing films of semiconductor and metal nanopart ....Early-Stage Medical Diagnostics by Plasmon-Mediated Gas Sensing. This project will investigate the use plasmonic absorption of light in metal nanostructures to activate the selective oxidation/reduction of a gas molecule on a semiconductor nanoparticle. This concept will be used with the aim of developing a sensing technique capable of measuring ultra-low concentrations (ppb) of breath markers for lung cancer detection. It is expected that porous sensing films of semiconductor and metal nanoparticles with well-defined light absorption properties will be fabricated. Superior selectivity will be achieved by matching the wavelength of the absorbed light with the required activation energy for oxidation/reduction. Successful outcomes will enable multi-analyte fingerprint identification by on-chip devices with applications ranging from portable medical diagnostics to national security.Read moreRead less