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Research Topic : Population-based
Field of Research : Applied Statistics
Australian State/Territory : ACT
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  • Funded Activity

    Discovery Projects - Grant ID: DP150104405

    Funder
    Australian Research Council
    Funding Amount
    $240,158.00
    Summary
    The demographic consequences of migration to, from and within Australia. The long-term demographic consequences of migration to, from and within Australia, and the dynamic pathways that produced them, will be studied. This will involve the identification of the specific contributions made by international and internal migration to the age and sex population compositions of nine birthplace-specific populations from 1981 to 2011. To do this, publically available data will be collected and augmente .... The demographic consequences of migration to, from and within Australia. The long-term demographic consequences of migration to, from and within Australia, and the dynamic pathways that produced them, will be studied. This will involve the identification of the specific contributions made by international and internal migration to the age and sex population compositions of nine birthplace-specific populations from 1981 to 2011. To do this, publically available data will be collected and augmented with statistical methods to provide a complete, consistent account of population change for around 60 subnational areas. As migration and population change underpins many aspects of societal change in Australia, this research aims to provide an invaluable resource to other scientists and policy makers.
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    Funded Activity

    Discovery Projects - Grant ID: DP140101259

    Funder
    Australian Research Council
    Funding Amount
    $351,000.00
    Summary
    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.
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    Funded Activity

    Discovery Projects - Grant ID: DP180100836

    Funder
    Australian Research Council
    Funding Amount
    $359,083.00
    Summary
    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.
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