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
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0775510
Funder
Australian Research Council
Funding Amount
$400,000.00
Summary
Australian Social Science Data Archive: Network Extension and Sub-archive Development. The Australian Social Science Data Archive is a national facility that allows all researchers and members of the public to access a wide range of social science data sets for on-line analysis. The archive contains data that covers forty years of social, political and economic surveys. The archive also acts as a gateway for social science researchers to access data from equivalent overseas institutions in North ....Australian Social Science Data Archive: Network Extension and Sub-archive Development. The Australian Social Science Data Archive is a national facility that allows all researchers and members of the public to access a wide range of social science data sets for on-line analysis. The archive contains data that covers forty years of social, political and economic surveys. The archive also acts as a gateway for social science researchers to access data from equivalent overseas institutions in North America, the European Union and OECD countries.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0560677
Funder
Australian Research Council
Funding Amount
$416,902.00
Summary
Australian Social Science Data Archive: Facility Enhancement & Network Development. This project will enhance Australia's social science research infrastructure by creating a distributed data archive with world class cataloguing, online access and analysis capabilities. It will also pilot a complementary qualitative data archive. The facility will provide improved archiving, access and online analysis to the Australian research community, and enable researchers on eight Australian and internatio ....Australian Social Science Data Archive: Facility Enhancement & Network Development. This project will enhance Australia's social science research infrastructure by creating a distributed data archive with world class cataloguing, online access and analysis capabilities. It will also pilot a complementary qualitative data archive. The facility will provide improved archiving, access and online analysis to the Australian research community, and enable researchers on eight Australian and international projects to construct consolidated purpose-built datasets for their research and dissemination to Australian researchers. The qualitative archive will develop and pilot new technology for archiving, disseminating and analysing non-numeric social data as proof of concept for the development of a national qualitative archive.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0989083
Funder
Australian Research Council
Funding Amount
$550,000.00
Summary
Australian Social Science Data Archive: Provision of Advanced Research Infrastructure and Collaborative Environment. The Australian Social Science Data Archive (ASSDA) supports researchers in a wide range of social science and humanities disciplines. These researchers are both primary and secondary users of data collected across a range of economic, social, political and cultural areas. Increasingly, complex public policy problems require multi-disciplinary solutions based on a range of data sou ....Australian Social Science Data Archive: Provision of Advanced Research Infrastructure and Collaborative Environment. The Australian Social Science Data Archive (ASSDA) supports researchers in a wide range of social science and humanities disciplines. These researchers are both primary and secondary users of data collected across a range of economic, social, political and cultural areas. Increasingly, complex public policy problems require multi-disciplinary solutions based on a range of data sources to address these problems. This proposal provides a means for Australia's leading edge researchers to advance the knowledge base that can lead to the development of strong evidence based policy. The open access policies of ASSDA ensures that the general public, media, non-government organisation (NGOs) and government agencies are able to examine the public use data sets that are used by researchers to arrive at their conclusions.Read moreRead less
Air pollution: do modern statistical model selection techniques make the silent killer speak too loud? Air pollution is estimated to cause 2400 deaths annually in Australia with an associated cost to the community of $17.2 billion. The outcomes of this project will enable an improved understanding of the association between air pollution and mortality in Australia, thereby allowing government, public health authorities, and regulatory agencies to implement better air pollution standards and pro ....Air pollution: do modern statistical model selection techniques make the silent killer speak too loud? Air pollution is estimated to cause 2400 deaths annually in Australia with an associated cost to the community of $17.2 billion. The outcomes of this project will enable an improved understanding of the association between air pollution and mortality in Australia, thereby allowing government, public health authorities, and regulatory agencies to implement better air pollution standards and provide more informed advice to the public on the necessity of avoiding exposure to air pollutants. These two outcomes are particularly important given Australia's ageing population and the fact that the elderly are among those most susceptible to harm from air pollution exposure.Read moreRead less
Min/Max Autocorrelation Factors in Time Series Studies of the Adverse Health Effects of Ozone. The annual health costs associated with exposure to air pollution in Australia have been estimated at between $3 and 5.3 billion. Given these costs, it is vital to conduct research that ensures public health officials and policy makers stay fully informed of Australia’s air pollution problem. The project proposes to address this need by developing methodology to detect trends in air pollution concentra ....Min/Max Autocorrelation Factors in Time Series Studies of the Adverse Health Effects of Ozone. The annual health costs associated with exposure to air pollution in Australia have been estimated at between $3 and 5.3 billion. Given these costs, it is vital to conduct research that ensures public health officials and policy makers stay fully informed of Australia’s air pollution problem. The project proposes to address this need by developing methodology to detect trends in air pollution concentrations and reduce measurement error in recorded air pollution concentrations. This will enable relevant authorities to produce more accurate estimates of air pollution health costs and implement more appropriate pollution regulations and health warnings.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
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