Enhancing social research in Australia using dual-frame telephone surveys. The growing surge in mobile phones and mobile-phone only households has had a significant impact on the representativeness of social surveys and accuracy of social outcome measures. This project will develop methods for generating sampling lists of both types of telephone numbers to improve population coverage and accuracy of outcome measures.
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
Discovery Early Career Researcher Award - Grant ID: DE160101565
Funder
Australian Research Council
Funding Amount
$330,000.00
Summary
Flexible data modelling via skew mixture models:challenges and applications. This project seeks to explore new models for handling data with non-normal features. Parametric distributions are fundamental to statistical modelling and inference. For centuries, the ‘normal’ distribution has been the dominant model for continuous data. However, real data rarely satisfy the assumption of normality. There is thus a strong demand for more flexible distributions. This project aims to develop new methodol ....Flexible data modelling via skew mixture models:challenges and applications. This project seeks to explore new models for handling data with non-normal features. Parametric distributions are fundamental to statistical modelling and inference. For centuries, the ‘normal’ distribution has been the dominant model for continuous data. However, real data rarely satisfy the assumption of normality. There is thus a strong demand for more flexible distributions. This project aims to develop new methodologies in finite mixture modelling using skew component distributions to provide better models for handling data with non-normal features (such as skewness, heavy/light tails, and multimodality). Applications may include security intrusion detection, clinical diagnosis and prognosis, and flow and mass cytometry.Read moreRead less
Market segmentation methodology: attacking the 'Too Hard' basket. Businesses embrace market segmentation to identify and target clients. However, poor segmentation analysis leads to poor segment choice. This project will develop tools to improve segmentation analysis and will test the resulting tools in tourism, foster care and climate change mitigating behaviours, and produce usable, transferable recommendations.
Statistical and mathematical modelling to improve health care outcomes in hospitals. The aim of this project is to develop new quantitative techniques based on mathematical and statistical modelling that improve the outcomes of health care in hospitals. Hospital outcomes for patients are sub-optimal due to adverse events such as hospital acquired infections and fully stretched facilities. Research from this project will lead to resource usage being optimised using operations research; the tra ....Statistical and mathematical modelling to improve health care outcomes in hospitals. The aim of this project is to develop new quantitative techniques based on mathematical and statistical modelling that improve the outcomes of health care in hospitals. Hospital outcomes for patients are sub-optimal due to adverse events such as hospital acquired infections and fully stretched facilities. Research from this project will lead to resource usage being optimised using operations research; the transmission of hospital acquired infections being better understood using mathematical models; and better monitoring of adverse events and analyses of studies using statistical tools. Opportunities will be provided for hospital staff to acquire knowledge of the significance of these outcomes .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.
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ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights. In today's world, massive amounts of data in a variety of forms are collected daily from a multitude of sources. Many of the resulting data sets have the potential to make vital contributions to society, business and government, as well as impact on international developments, but are so large or complex that they are difficult to process and analyse using traditional tools. The aim of this ....ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights. In today's world, massive amounts of data in a variety of forms are collected daily from a multitude of sources. Many of the resulting data sets have the potential to make vital contributions to society, business and government, as well as impact on international developments, but are so large or complex that they are difficult to process and analyse using traditional tools. The aim of this Centre is to create innovative mathematical and statistical models that can uncover the knowledge concealed within the size and complexity of these big data sets, with a focus on using the models to deliver insight into problems vital to the Centre's Collaborative Domains: Healthy People, Sustainable Environments and Prosperous Societies.Read moreRead less