Imaging circumstellar matter at high resolution. Within contemporary astrophysics there is a particular fascination with matter in near-stellar environments. Studies of stellar and planetary systems from formation through to eventual destruction entail observation of material, principally dust and gas, playing their parts on a very remote stage. A new generation of telescopes, known as interferometers, deliver extremely high resolutions enabling our first direct glimpses of these phenomena. Here ....Imaging circumstellar matter at high resolution. Within contemporary astrophysics there is a particular fascination with matter in near-stellar environments. Studies of stellar and planetary systems from formation through to eventual destruction entail observation of material, principally dust and gas, playing their parts on a very remote stage. A new generation of telescopes, known as interferometers, deliver extremely high resolutions enabling our first direct glimpses of these phenomena. Here I propose using a number of these devices in concert in order to dramatically enhance their scientific payoff. In addition to enabling unique studies of stellar systems, new techniques for merging disparate data into powerful combined forms will be devised.Read moreRead less
Innovations in Bayesian likelihood-free inference. Bayesian inference is a statistical method of choice in applied science. This project will develop innovative tools which permit Bayesian inference in problems considered intractable only a few years ago. These methods will expedite advances in multidisciplinary research across a range of applications. With these foundations, this project will accelerate national research efforts into improving frameworks for projecting trends in water availabil ....Innovations in Bayesian likelihood-free inference. Bayesian inference is a statistical method of choice in applied science. This project will develop innovative tools which permit Bayesian inference in problems considered intractable only a few years ago. These methods will expedite advances in multidisciplinary research across a range of applications. With these foundations, this project will accelerate national research efforts into improving frameworks for projecting trends in water availability and management, the impact of climate extremes, telecommunications engineering, HIV and infectious disease modelling and biostatistics. With many sectors unable to recruit appropriately trained statisticians within Australia, this project will train four PhD students in Bayesian statistics.
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Statistical methods for analysing multi-source microarray data and building gene regulatory networks. I will devise a statistical learning technique that does not force a gene to be assigned to exactly one category. This technique reflects the biological reality that a gene can belong to two or more functional categories. Therefore, the new technique will improve a model's ability to identify regulatory genes in different types of cancer; these regulatory genes can be targeted by new anti-cancer ....Statistical methods for analysing multi-source microarray data and building gene regulatory networks. I will devise a statistical learning technique that does not force a gene to be assigned to exactly one category. This technique reflects the biological reality that a gene can belong to two or more functional categories. Therefore, the new technique will improve a model's ability to identify regulatory genes in different types of cancer; these regulatory genes can be targeted by new anti-cancer drugs resulting in a more effective treatment. I will model gene regulatory networks using microarray data from multiple sources. These networks will be used to identify regulatory cliques - a group of genes that are vital for a cellular function. This will improve our understanding of debilitating conditions such as asthma.Read moreRead less
Asymptotic Expansions and Large Deviations in Probability and Statistics: Theory and Applications. Statistics is the major enabling science in a number of disciplines. This is fundamental research in probability and statistics but it has wide applications in Biology and Social Sciences which will ultimately be of national benefit. The behaviour of self normalized sums is an exciting new area of fundamental research that has implications for the application of statistics in many areas. U-statist ....Asymptotic Expansions and Large Deviations in Probability and Statistics: Theory and Applications. Statistics is the major enabling science in a number of disciplines. This is fundamental research in probability and statistics but it has wide applications in Biology and Social Sciences which will ultimately be of national benefit. The behaviour of self normalized sums is an exciting new area of fundamental research that has implications for the application of statistics in many areas. U-statistics for dependent situations has direct application to understanding financial time series and the analysis of sample survey data. Saddlepoint methods provide extremely accurate approximations in a number of important applications.
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Empirical saddlepoint approximations and self-normalized limit theorems. Finite population sampling and resampling methods such as the bootstrap and randomization methods are central in a number of areas of application and M-estimates are the major method used to give robust methods under mild conditions; in both these areas statistics are used which are Studentized or self-normalized. We will develop asymptotic approaches for such statistics. Saddlepoint and empirical saddlepoint methods will ....Empirical saddlepoint approximations and self-normalized limit theorems. Finite population sampling and resampling methods such as the bootstrap and randomization methods are central in a number of areas of application and M-estimates are the major method used to give robust methods under mild conditions; in both these areas statistics are used which are Studentized or self-normalized. We will develop asymptotic approaches for such statistics. Saddlepoint and empirical saddlepoint methods will be used to give methods which have second order relative accuracy in large deviation regions and we will obtain limit results and Edgeworth approximations. Emphasis will be on obtaining results under weak conditions necessary for applications.Read moreRead less
Choice experiments to improve predictive power for policy makers. In the current economic climate, Australian governments will benefit from superior choice experiments which will lead to improved prediction of the potential public benefit of proposed policy changes. The choice experiments developed here will have a substantial effect on the development of strategies for the promotion and maintenance of a strong health care system as well as being relevant to the maintenance of a sustainable envi ....Choice experiments to improve predictive power for policy makers. In the current economic climate, Australian governments will benefit from superior choice experiments which will lead to improved prediction of the potential public benefit of proposed policy changes. The choice experiments developed here will have a substantial effect on the development of strategies for the promotion and maintenance of a strong health care system as well as being relevant to the maintenance of a sustainable environment, both designated National Research Priority areas. The innovative research proposed will tap into and build strong links with international research networks, advancing Australia's research reputation and providing a rich environment for the training of research graduates.Read moreRead less
New Bayesian methodology for understanding complex systems using hidden Markov models and expert opinion, environmental, robotics and genomics applications. This project aims to merge four areas of intense international interest in describing complex systems: hidden Markov models and mixtures, semi-parametric and nonparametric approaches, true combination of expert opinion with data, and new Bayesian computational methods based on perfect sampling and particle sampling. The project will signific ....New Bayesian methodology for understanding complex systems using hidden Markov models and expert opinion, environmental, robotics and genomics applications. This project aims to merge four areas of intense international interest in describing complex systems: hidden Markov models and mixtures, semi-parametric and nonparametric approaches, true combination of expert opinion with data, and new Bayesian computational methods based on perfect sampling and particle sampling. The project will significantly contribute to statistical methodology and its ability to inform about real-world problems. A strong focus on applications to genomics, robotics and environmental modelling will bring immediate research and monetary benefit for industry. Expected outcomes include enhanced cross-disciplinary and international linkages, publications, industry-funded projects and highly trained graduates.Read moreRead less
Frontiers in Data Science: Analysing Distributions as Data. This project aims to develop the statistical foundations of a new approach to analysing large and complex data, based on building distributional approximations of the data, which can then be analysed by standard statistical methods. The need to analyse very large and complex datasets has become a vital part of everyday life, particularly in the analysis of national problems in public health, environmental pollution, computer network sec ....Frontiers in Data Science: Analysing Distributions as Data. This project aims to develop the statistical foundations of a new approach to analysing large and complex data, based on building distributional approximations of the data, which can then be analysed by standard statistical methods. The need to analyse very large and complex datasets has become a vital part of everyday life, particularly in the analysis of national problems in public health, environmental pollution, computer network security and climate extremes. The project expects to change our way of thinking in how to be smarter about what data we use (and collect) for analysis, rather than relying on brute force analysis of large datasets. The project is expected to transform the knowledge base of the discipline, and the resulting techniques will enable across-the-board research advances for many industries and disciplines.Read moreRead less
Statistical methods and tools for integrative microarray analysis. Tools used for biological and medical research have been evolving and there has been an increase in high-throughput technologies such as genome sequencing and DNA microarray. The growing number of entries and the increasing availability of public microarray repositories and other sequence databases have generated the new challenge of developing tools to efficiently integrate data by different research groups. This research provi ....Statistical methods and tools for integrative microarray analysis. Tools used for biological and medical research have been evolving and there has been an increase in high-throughput technologies such as genome sequencing and DNA microarray. The growing number of entries and the increasing availability of public microarray repositories and other sequence databases have generated the new challenge of developing tools to efficiently integrate data by different research groups. This research provides new statistical methods to integrate different data sets. Its application in the biomedical field will allow researchers to effectively interpret the myriad of data generated within the community.Read moreRead less
Statistical and computational methods using a multiscale approach for protein identification and quantification. Proteins are critically important in the onset and ongoing illness associated with disease. Key proteins may serve as markers to diagnose or predict the course of a disease, or even become the target of pharmaceuticals. Accurate, efficient and robust algorithms are a critical component in protein identification. This research provides novel statistical algorithms for protein identific ....Statistical and computational methods using a multiscale approach for protein identification and quantification. Proteins are critically important in the onset and ongoing illness associated with disease. Key proteins may serve as markers to diagnose or predict the course of a disease, or even become the target of pharmaceuticals. Accurate, efficient and robust algorithms are a critical component in protein identification. This research provides novel statistical algorithms for protein identification using multiscale analysis techniques. Their applications in the bio-medical field will enable Australian and international researchers to identify key proteins more accurately, than current methods, leading to improve health, medical, and biological research outcomes.Read moreRead less