Attrition in longitudinal studies: advancing and evaluating statistical methods. Longitudinal studies are a vital tool for monitoring the health and well-being of Australians. They are uniquely placed to examine changes in diseases over time and prospectively collect data on exposure and disease onset. There have been many successful longitudinal studies in Australia that have lead to significant breakthroughs in evidence-based health (e.g. the Nambour Skin Cancer Prevention Trial). Unfortunatel ....Attrition in longitudinal studies: advancing and evaluating statistical methods. Longitudinal studies are a vital tool for monitoring the health and well-being of Australians. They are uniquely placed to examine changes in diseases over time and prospectively collect data on exposure and disease onset. There have been many successful longitudinal studies in Australia that have lead to significant breakthroughs in evidence-based health (e.g. the Nambour Skin Cancer Prevention Trial). Unfortunately all longitudinal studies suffer from attrition, or loss of participants, which leads to questions concerning their validity and generalisability. This project will investigate the causes of attrition, and the effect attrition has on longitudinal studies, in order to improve their design and analysis.Read moreRead less
Mixture models for high-dimensional clustering with applications to tumour classification, network intrusion, and text classification. This project will benefit the Australian Society as a whole by developing statistical methodology for the clustering of high-dimensional data. In particular, it will develop a novel and efficient model for extracting useful information from subpopulations. It thus has wide applicability to improving the quality and validity of applied research in most industries ....Mixture models for high-dimensional clustering with applications to tumour classification, network intrusion, and text classification. This project will benefit the Australian Society as a whole by developing statistical methodology for the clustering of high-dimensional data. In particular, it will develop a novel and efficient model for extracting useful information from subpopulations. It thus has wide applicability to improving the quality and validity of applied research in most industries in Australia. More specifically, it is to be applied here to classify brain tumours and detect network intruders. This cross-disciplinary project will contribute to Australia's economic of public health, protect Australia from crime, and strength Australian researchers' capacity and capability of participating in this emerging science.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
Discovery Early Career Researcher Award - Grant ID: DE130100819
Funder
Australian Research Council
Funding Amount
$281,600.00
Summary
Measuring the improbable: optimal Monte Carlo methods for rare event simulation of maxima of dependent random variables. Some events occurring with low frequency can have dramatic consequences: natural catastrophes, economic crises, system malfunctions. Estimating their probabilities is a very difficult problem. This project will develop new simulation methods capable of delivering the most precise and efficient estimators for the probabilities of such events.
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0453870
Funder
Australian Research Council
Funding Amount
$102,900.00
Summary
Social science advanced data modelling, analysis and visualisation facility. This is an integrated facility for advanced social science data analysis, modelling and visualisation located at The University of Queensland. The collaborating institutions are UQ, ANU and Griffith University. The facility promotes deep collaborations between social scientists and quantitative methodologists (statisticians, biostatisticians, econometricians) to enable leading edge quantitative analyses of survey, spati ....Social science advanced data modelling, analysis and visualisation facility. This is an integrated facility for advanced social science data analysis, modelling and visualisation located at The University of Queensland. The collaborating institutions are UQ, ANU and Griffith University. The facility promotes deep collaborations between social scientists and quantitative methodologists (statisticians, biostatisticians, econometricians) to enable leading edge quantitative analyses of survey, spatial and population data, new developments in quantitative methodology for statistical modelling and data visualisation, enhanced international research linkages and advanced postgraduate research training.Read moreRead less
Generalised Degrees of Freedom and Probabilistic Regularisation. This project intends to develop novel statistical tools for more accurate prediction by taking account of model complexity and uncertainties associated with the fitting procedure. The project also plans to develop a novel shrinkage approach via new penalty functions to avoid over-fitting and asymptotic properties. The key applications may include genetic studies where the number of predictors is large and biological experiments whe ....Generalised Degrees of Freedom and Probabilistic Regularisation. This project intends to develop novel statistical tools for more accurate prediction by taking account of model complexity and uncertainties associated with the fitting procedure. The project also plans to develop a novel shrinkage approach via new penalty functions to avoid over-fitting and asymptotic properties. The key applications may include genetic studies where the number of predictors is large and biological experiments where multivariate and temporal data are often collected – for example economical breeding in animal and fish farming and more effectively detecting the genes of interest in genetic studies on human, animals and plants.Read moreRead less
Random network models with applications in biology. Complex biological systems consist of a large number of interacting agents or components, and so can be studied using mathematical random network models. We aim to gain deeper insights into the laws emerging as the random networks evolve in time. This can help us to deal with dangerous disease epidemics and better understand the human brain.
Robust inferences for analysis of longitudinal data. This project will develop novel statistical tools. Outcomes of this project will enable more reliable data analysis and more cost effective designs in environmental and biological studies.
Precision ecology: the modern era of designed experiments in plant ecology. This project aims to develop the field of precision ecology, forging a new era of designed experiments where sampling is informed by research questions and what is known about the ecological process being studied. Through the development of novel statistical methods, new experiments globally will be designed to answer important ecological questions including what influence abiotic and biotic factors have on plant commun ....Precision ecology: the modern era of designed experiments in plant ecology. This project aims to develop the field of precision ecology, forging a new era of designed experiments where sampling is informed by research questions and what is known about the ecological process being studied. Through the development of novel statistical methods, new experiments globally will be designed to answer important ecological questions including what influence abiotic and biotic factors have on plant communities over time and different spatial scales. Expected outcomes include new methods and tools that will modernise how future experiments will be conducted in plant ecology. This will provide significant transdisciplinary benefits including new statistical methods that target scientific discovery in ecological studies.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE160100741
Funder
Australian Research Council
Funding Amount
$382,274.00
Summary
Tractable Bayesian algorithms for intractable Bayesian problems. This project seeks to develop computationally efficient and scalable Bayesian algorithms to estimate the parameters of complex models and ensure inferences drawn from the models can be trusted. Bayesian parameter estimation and model validation procedures are currently computationally intractable for many complex models of interest in science and technology. These include biological processes such as the efficacy of heart disease, ....Tractable Bayesian algorithms for intractable Bayesian problems. This project seeks to develop computationally efficient and scalable Bayesian algorithms to estimate the parameters of complex models and ensure inferences drawn from the models can be trusted. Bayesian parameter estimation and model validation procedures are currently computationally intractable for many complex models of interest in science and technology. These include biological processes such as the efficacy of heart disease, wound healing and skin cancer treatments. Potential outcomes of the project include new algorithms to significantly economise computations and improved understanding of the mechanisms of experimental data generation. Improved models of wound healing, skin cancer growth and heart physiology supported by these algorithms could improve population health.Read moreRead less