Efficient Design for Generalized Linear Models. In industrial, commercial and social research, we collect data in order to predict the outcome of a process based on the inputs to that process. We want to maximize the information that is gained from the data. Good planning is crucially important to achieve this. This project will determine how best to select the inputs to the process for many situations that occur in research. A computer package to answer these questions will be written. The nati ....Efficient Design for Generalized Linear Models. In industrial, commercial and social research, we collect data in order to predict the outcome of a process based on the inputs to that process. We want to maximize the information that is gained from the data. Good planning is crucially important to achieve this. This project will determine how best to select the inputs to the process for many situations that occur in research. A computer package to answer these questions will be written. The nation will benefit from a fundamental increase in efficiency of research and, therefore, in efficient use of research dollars.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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Bayesian Inference for Multivariate Hierarchical Regression Models. This project will develop Bayesian methodology for analysing multivariate regression models. The distribution of each measurement can be discrete or continuous, with the dependence between measurements obtained through the correlation matrix of a Gaussian copula. Model parsimony is obtained by identifying zero elements in the correlation matrix or its inverse and by variable selection on the regression parameters. The results wi ....Bayesian Inference for Multivariate Hierarchical Regression Models. This project will develop Bayesian methodology for analysing multivariate regression models. The distribution of each measurement can be discrete or continuous, with the dependence between measurements obtained through the correlation matrix of a Gaussian copula. Model parsimony is obtained by identifying zero elements in the correlation matrix or its inverse and by variable selection on the regression parameters. The results will be applied to solve problems in finance, health management and marketing. In all these fields multiple observations are often taken per individual or time period and the models need to incorporate measures of dependence and uncertainty.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
Investment Approaches and Applications in Financial Markets: Evolutionary Kernel Based Subset Time-Series Using Semi-Parametric Approaches. The project will develop new investment assessments based on subset time-series modeling. Innovative evolutionary kernel smoothing algorithms using semi-parametric approaches will be introduced. The project will make three important applications of this modeling in financial markets: a) benchmarking and evaluation of inflation-indexed bonds; b) evaluation of ....Investment Approaches and Applications in Financial Markets: Evolutionary Kernel Based Subset Time-Series Using Semi-Parametric Approaches. The project will develop new investment assessments based on subset time-series modeling. Innovative evolutionary kernel smoothing algorithms using semi-parametric approaches will be introduced. The project will make three important applications of this modeling in financial markets: a) benchmarking and evaluation of inflation-indexed bonds; b) evaluation of the performance of global diversified investment funds; and c) prediction to provide early warning of the emergence of destabilising deflation or inflation. These three applications will lead to improved risk management practices and investment performance. Recursive algorithms will provide new statistical methods to study investment asset price movements and market volatility.
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Investment approaches and opportunities in renewable energy and financial resource markets, using semi-parametric approaches to evolutionary subset time-series lattice-ladder modelling. The project findings will help Australian exporters and importers understand and manage energy and resource price risks more effectively. The investment community will benefit through selecting optimal asset allocations and enhancing value to investors. It will also benefit many other agencies, particularly in th ....Investment approaches and opportunities in renewable energy and financial resource markets, using semi-parametric approaches to evolutionary subset time-series lattice-ladder modelling. The project findings will help Australian exporters and importers understand and manage energy and resource price risks more effectively. The investment community will benefit through selecting optimal asset allocations and enhancing value to investors. It will also benefit many other agencies, particularly in the service industries. It is not well recognised that in developed countries, including Australia, the financial service and related sectors account for more than 60 percent of economic activity and employment, so it is critical that more sophisticated statistical methods be established, and practical applications conducted, in order to advance the understanding of complexity management in the financial service and related sectors.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
Frontiers in Bayesian methods for computationally intractable models. This project aims to develop new statistical methods for the analysis of computationally intractable models. Direct statistical analyses are often impossible when models are highly complex or too large, or when the dataset is too big. Using simpler models means that the wrong questions are being answered. Using less data is wasteful of information. This project aims to develop new methods for Bayesian statistical inference whe ....Frontiers in Bayesian methods for computationally intractable models. This project aims to develop new statistical methods for the analysis of computationally intractable models. Direct statistical analyses are often impossible when models are highly complex or too large, or when the dataset is too big. Using simpler models means that the wrong questions are being answered. Using less data is wasteful of information. This project aims to develop new methods for Bayesian statistical inference when standard methods are intractable. By allowing otherwise unavailable analyses, such techniques can enable and accelerate across-the-board research advances. Key expected innovations are new bases for sampling from computationally intractable distributions and novel distributed computing algorithms which could be applied to problems in health economics to infectious disease modelling, from climate extremes to road traffic modelling.Read moreRead less