Bayesian inference for complex regression models using mixtures. The project will use mixtures to flexibly model complex regression functions and will develop Bayesian methods for carrying out statistical inference on these models. The models will deal with both Gaussian and non-Gaussian data. Multiple explanatory variables are dealt with by mixing simple additives to produce flexible high dimensional function estimates. Variable selection and model averaging will be used to identify important v ....Bayesian inference for complex regression models using mixtures. The project will use mixtures to flexibly model complex regression functions and will develop Bayesian methods for carrying out statistical inference on these models. The models will deal with both Gaussian and non-Gaussian data. Multiple explanatory variables are dealt with by mixing simple additives to produce flexible high dimensional function estimates. Variable selection and model averaging will be used to identify important variables and thus make the estimation more efficient. The methods will be extended to multivariate responses where account will taken be taken of the structure of the dependence between responses.Read moreRead less
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
Statistical Methods for Flow Cytometric Data. The project will aid users of flow cytometry throughout Australia. It will help foster collaborations between the biological and mathematical scientists. Biological research is an important part of Australia's future and is becoming very quantitative. During the course of the project, two PhD students will be provided strong training in Statistics geared towards biological applications. The project is aligned with the 8th Human Leucocyte Differentiat ....Statistical Methods for Flow Cytometric Data. The project will aid users of flow cytometry throughout Australia. It will help foster collaborations between the biological and mathematical scientists. Biological research is an important part of Australia's future and is becoming very quantitative. During the course of the project, two PhD students will be provided strong training in Statistics geared towards biological applications. The project is aligned with the 8th Human Leucocyte Differentiation Antigen workshop to culminate in Adelaide in December 2004 and will aid the fight against blood cell cancers. The project will also aid research on plankton with potential commercial benefits for Australia's marine scallop industry.
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Improved Monte Carlo Methods for Estimation, Optimisation and Counting. The project will benefit the Australian society by building the theoretical and methodological foundations for the next generation of Monte Carlo techniques. The advancement of the knowledge in this area will provide important tools for solving complex estimation, optimisation and counting problems in engineering, statistics, computer science, mathematics and the physical and life sciences. As a result it will generate a com ....Improved Monte Carlo Methods for Estimation, Optimisation and Counting. The project will benefit the Australian society by building the theoretical and methodological foundations for the next generation of Monte Carlo techniques. The advancement of the knowledge in this area will provide important tools for solving complex estimation, optimisation and counting problems in engineering, statistics, computer science, mathematics and the physical and life sciences. As a result it will generate a competitive advantage for various sections of the Australian industry, including telecommunications, biotechnology and finance. The project will enable Australian researchers to continue to work at the forefront of this fast moving and exciting area of international research.Read moreRead less
Rare Event Simulation with Heavy Tails. The project provides a rigorous way to enhance our understanding of the mechanisms that bring about catastrophic rare events such as urban flooding, electricity shortages and financial bankrupcy. Australia is at the forefront of exciting recent developments in rare event simulation. The advancement of the knowledge in this area will generate a competitive advantage for various sections of the Australian industry, including the areas of industrial reliabili ....Rare Event Simulation with Heavy Tails. The project provides a rigorous way to enhance our understanding of the mechanisms that bring about catastrophic rare events such as urban flooding, electricity shortages and financial bankrupcy. Australia is at the forefront of exciting recent developments in rare event simulation. The advancement of the knowledge in this area will generate a competitive advantage for various sections of the Australian industry, including the areas of industrial reliability, finance and insurance, were accurate simulation techniques are becoming increasingly important.Read moreRead less