New methods for small group analysis from sample surveys. National and state averages of statistics on issues such as unemployment, salinity, drought impact, and health often hide large differences between population sub-groups and between small areas. This local variation needs to be understood so that effective policies can be developed and carried out efficiently and their impact monitored. This project will provide, for the first time, robust and efficient methods for providing information o ....New methods for small group analysis from sample surveys. National and state averages of statistics on issues such as unemployment, salinity, drought impact, and health often hide large differences between population sub-groups and between small areas. This local variation needs to be understood so that effective policies can be developed and carried out efficiently and their impact monitored. This project will provide, for the first time, robust and efficient methods for providing information on these variations using data from large-scale national and state surveys. This will lead to significant improvements in the data available for small population groups and small areas, allowing better targeting of policies aimed at addressing local differences.Read moreRead less
Handling Missing Data in Complex Household Surveys. The Australian Bureau of Statistics (ABS) has an extensive program of household surveys that is a key source of information on the social and economic conditions of the population. They provide statistics and data on a large range of social and economic topics, such as health, education, the labour force, income and expenditure. Analysis of household survey data by a variety of organisations underpins policy development and evaluation and the e ....Handling Missing Data in Complex Household Surveys. The Australian Bureau of Statistics (ABS) has an extensive program of household surveys that is a key source of information on the social and economic conditions of the population. They provide statistics and data on a large range of social and economic topics, such as health, education, the labour force, income and expenditure. Analysis of household survey data by a variety of organisations underpins policy development and evaluation and the expenditure of billions of dollars. This project will substantially improve the cost-efficiency and reliability of Australian household survey data, by creating new approaches for handling missing data that deal with the realities of typical household surveys.Read moreRead less
Prediction, inference and their application to modelling correlated data. This project aims to create new, improved methods for prediction and making inference about predictions for a variety of correlated data types through inventing sophisticated and novel resampling schemes such as the generalised fast bootstrap and repeated partial permutation. The research will impact on both the theory and practice of statistics and on substantive fields which use mixed or compositional models to analyse d ....Prediction, inference and their application to modelling correlated data. This project aims to create new, improved methods for prediction and making inference about predictions for a variety of correlated data types through inventing sophisticated and novel resampling schemes such as the generalised fast bootstrap and repeated partial permutation. The research will impact on both the theory and practice of statistics and on substantive fields which use mixed or compositional models to analyse dependent data. This will be a significant improvement in the assessment and stability of statistical models in areas such as social, ecological and geological sciences.Read moreRead less
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
Dimension reduction and model selection for statistically challenging data. This project aims to develop a deep theoretical understanding of the relationship between various dimension reduction and model selection methods used in statistical model building, and then use this understanding to develop new, improved methods of model building for statistically challenging data. The research will impact on both the theory and practice of statistics, and on substantive fields which collect and analyse ....Dimension reduction and model selection for statistically challenging data. This project aims to develop a deep theoretical understanding of the relationship between various dimension reduction and model selection methods used in statistical model building, and then use this understanding to develop new, improved methods of model building for statistically challenging data. The research will impact on both the theory and practice of statistics, and on substantive fields which collect and analyse these kinds of data. This will provide a significant improvement in the statistical model building in areas such as epidemiology, chemical and ecological sciences. The project is timely because of the increasing collection of large-dimensional, complex, correlated data sets in these and many other fields.Read moreRead less
Building models for complex data. The purpose of this project is to better understand the process of building statistical models and construct new methods for building models for particular kinds of complex data. The expected outcomes include a new way of thinking about model building and practical tools which together enable us to get more value out of analysing complex data.
Novel statistical methods for data with non-Euclidean geometric structure. This project aims to develop new flexible regression models and classification algorithms, along with robust and efficient inference methods, applicable to a wide range of non-Euclidean data types which arise in many fields of science, business and technology. There are serious flaws with currently available methods of analysis for non-Euclidean data. This project expects to transform such analyses by providing new quanti ....Novel statistical methods for data with non-Euclidean geometric structure. This project aims to develop new flexible regression models and classification algorithms, along with robust and efficient inference methods, applicable to a wide range of non-Euclidean data types which arise in many fields of science, business and technology. There are serious flaws with currently available methods of analysis for non-Euclidean data. This project expects to transform such analyses by providing new quantitative tools within a unifying framework. The anticipated project outcomes will be of mathematical interest and valuable in applications such as finance (predicting Australian stock returns); modelling electroencephalography data; Australian geochemical data, relating to sediments; and Australian X-ray tumour image data. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180100220
Funder
Australian Research Council
Funding Amount
$369,075.00
Summary
Statistics for manifold-valued data. This project aims to develop, and then implement, a new suite of fully flexible, interpretable and tractable models for manifold-valued data, along with robust and accurate estimation techniques for their parameters. Multivariate data with complicated constraints, such as manifold-valued data, is frequently encountered in the physical, biological and medical sciences, however it is difficult to define tractable statistical models and estimate their parameters ....Statistics for manifold-valued data. This project aims to develop, and then implement, a new suite of fully flexible, interpretable and tractable models for manifold-valued data, along with robust and accurate estimation techniques for their parameters. Multivariate data with complicated constraints, such as manifold-valued data, is frequently encountered in the physical, biological and medical sciences, however it is difficult to define tractable statistical models and estimate their parameters due to the curvature and nonlinear geometry of the sample space. The outcomes of the project are of direct mathematical interest as well as having significant interest to science and business disciplines where manifold-valued data is commonly observed.Read moreRead less
Prognosis based network-type feature extraction for complex biological data. This project aims to develop statistical tools that integrate high-throughput molecular data with biological knowledge to make discoveries in complex diseases. This project uses machine learning methods, statistical models and proteomic platforms to identify relationships among clinico-pathologic and molecular measurements. It will produce tools and insights that are intended to accelerate the process of biologically an ....Prognosis based network-type feature extraction for complex biological data. This project aims to develop statistical tools that integrate high-throughput molecular data with biological knowledge to make discoveries in complex diseases. This project uses machine learning methods, statistical models and proteomic platforms to identify relationships among clinico-pathologic and molecular measurements. It will produce tools and insights that are intended to accelerate the process of biologically and clinically significant discoveries in biomedical research. This project will help Australian researchers in statistics and users of statistics (from fields as diverse as biology, ecology, medicine, finance, agriculture and the social sciences) to make better predictions that are easier to understand.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100435
Funder
Australian Research Council
Funding Amount
$365,039.00
Summary
Modern statistical methods for complex multivariate longitudinal data. The project aims to develop novel approaches for the statistical analysis of large, complex multivariate longitudinal data, and apply them to two datasets to address scientific questions related to the drivers and consequences of poor physical and mental health in Australia, and the spatio-temporal evolution of species assemblages in the Southern Ocean. The project expects to develop new knowledge in the areas of statistical ....Modern statistical methods for complex multivariate longitudinal data. The project aims to develop novel approaches for the statistical analysis of large, complex multivariate longitudinal data, and apply them to two datasets to address scientific questions related to the drivers and consequences of poor physical and mental health in Australia, and the spatio-temporal evolution of species assemblages in the Southern Ocean. The project expects to develop new knowledge in the areas of statistical model building, model selection, and inference for multivariate longitudinal data. This will lead to a suite of modern methods and insights for computationally efficient, mathematically rigorous statistical data analysis that, when applied, should provide significant benefits to public health and ecology.Read moreRead less