Classification of Microarray Gene-Expression Data. The broad aim is to provide statistical methodology for the classification of microarray gene-expression data. Microarrays are part of a new biotechnology that allows the monitoring of expression levels for thousands of genes simultaneously. The explosion in microarrays has produced massive quantities of data that require new statistical techniques for analysis in order to exploit their enormous scientific potential. One of the main uses of ....Classification of Microarray Gene-Expression Data. The broad aim is to provide statistical methodology for the classification of microarray gene-expression data. Microarrays are part of a new biotechnology that allows the monitoring of expression levels for thousands of genes simultaneously. The explosion in microarrays has produced massive quantities of data that require new statistical techniques for analysis in order to exploit their enormous scientific potential. One of the main uses of the methodology to be developed is to expedite the discovery of new subclasses of diseases. Another is to provide prediction rules for the diagnosis and treatment of diseases.Read moreRead less
Entropic Analysis of Financial Risk and Uncertainty. The recent financial crisis has shown that the financial markets are not as stable as expected, and are at risk from a lack of knowledge about new financial products and their risks. This research provides a framework to better measure and forecast financial risks by applying a set of techniques known collectively as entropic analysis as a novel way to measure the amount of information that can be extracted from historical data. The research w ....Entropic Analysis of Financial Risk and Uncertainty. The recent financial crisis has shown that the financial markets are not as stable as expected, and are at risk from a lack of knowledge about new financial products and their risks. This research provides a framework to better measure and forecast financial risks by applying a set of techniques known collectively as entropic analysis as a novel way to measure the amount of information that can be extracted from historical data. The research will facilitate the design of policies and regulations by regulatory authorities that need to evaluate new financial products, their associated risks and their impacts on the financial markets.Read moreRead less
Bayesian Inference for Flexible Parametric Multivariate Econometric Modelling. The anticipated outcomes include the development of enhanced multivariate econometric models and innovative computationally intensive methods for their estimation. These models are used in numerous and diverse applications which are data-intensive and where more complete models will greatly enhance data-based decision-making. Results include improved information use in the wholesale electricity markets, in financial m ....Bayesian Inference for Flexible Parametric Multivariate Econometric Modelling. The anticipated outcomes include the development of enhanced multivariate econometric models and innovative computationally intensive methods for their estimation. These models are used in numerous and diverse applications which are data-intensive and where more complete models will greatly enhance data-based decision-making. Results include improved information use in the wholesale electricity markets, in financial market investment decision-making and for the assessment of the impact of internet advertising.Read moreRead less
Bayesian estimation of flexible spatial models with applications in medical imaging and econometric modeling. This project aims to develop statistical methodology for estimating flexible highly parameterised Bayesian spatial models. The flexible models examined will include regression, choice and time series models for data that is spatially registered. Spatial smoothing of parameters in the models will involve application of hierarchical spatial prior distributions. The resulting methodology wi ....Bayesian estimation of flexible spatial models with applications in medical imaging and econometric modeling. This project aims to develop statistical methodology for estimating flexible highly parameterised Bayesian spatial models. The flexible models examined will include regression, choice and time series models for data that is spatially registered. Spatial smoothing of parameters in the models will involve application of hierarchical spatial prior distributions. The resulting methodology will be applied to the analysis of medical imaging data and to the estimation of spatial econometric models of residential real estate prices. The expected outcomes include developments in the frontier framework of Bayesian computational estimation methodology, improved methods for medical image processing and estimation of high resolution spatial models of residential real estate prices in Australian metropolitan centres.Read moreRead less
Assessing and enhancing the quality of longitudinal survey data. Australia has begun investing heavily in the collection of population-wide longitudinal survey data. Most of that effort has focused first on collection and dissemination and second on analysis, with scant attention paid to the quality of data collected. This is unfortunate given that longitudinal surveys exhibit many problems (e.g., attrition, panel conditioning, and seam effects) that are not relevant in more ubiquitous cross-sec ....Assessing and enhancing the quality of longitudinal survey data. Australia has begun investing heavily in the collection of population-wide longitudinal survey data. Most of that effort has focused first on collection and dissemination and second on analysis, with scant attention paid to the quality of data collected. This is unfortunate given that longitudinal surveys exhibit many problems (e.g., attrition, panel conditioning, and seam effects) that are not relevant in more ubiquitous cross-section of surveys. Without adequate resources devoted to these methodological issues, the quality of substantive research will be questioned and interest from potential users decline. Maximizing the investment being made in longitudinal data thus requires a complementary investment in methodological research.Read moreRead less
Modelling the Choices of Individuals. Individuals make decisions daily and some of these decisions have wide-reaching and long-term consequences, such as choices among housing, public transport, electoral candidates and health care options. The principal aim of this project is to develop reliable and valid ways to model individual level choice processes. Once completed, this will provide insights into ways to aggregate sampled observations when population-level applications are required, and all ....Modelling the Choices of Individuals. Individuals make decisions daily and some of these decisions have wide-reaching and long-term consequences, such as choices among housing, public transport, electoral candidates and health care options. The principal aim of this project is to develop reliable and valid ways to model individual level choice processes. Once completed, this will provide insights into ways to aggregate sampled observations when population-level applications are required, and allow us to compare and test several competing theories of choice behaviour. This will enable us to make contributions to understanding and modelling human decision making in many fields ranging from marketing to medicine.Read moreRead less
On-line and Incremental EM-based Neural Networks: Application to Hospital Utlilization and Gene Expression Data. Artificial neural networks have been widely applied as universal classifiers in many fields, such as biomedicine. However, misunderstanding of fundamental statistical principles, which can cause misleading findings, has been frequently observed in the literature. This project aims to integrate statistical methodologies in neural networks to provide a unified approach to improve its ....On-line and Incremental EM-based Neural Networks: Application to Hospital Utlilization and Gene Expression Data. Artificial neural networks have been widely applied as universal classifiers in many fields, such as biomedicine. However, misunderstanding of fundamental statistical principles, which can cause misleading findings, has been frequently observed in the literature. This project aims to integrate statistical methodologies in neural networks to provide a unified approach to improve its applicability and efficiency in implementation. The system developed from this proposed cross-disciplinary research will be applied to hospital utilization data (hospital morbidity database, Western Australia) and gene expression data (DNA microarrays databases, Harvard University). This collaborative research will advance the international standard of Australian research communities.
Read moreRead less