Reliable and accurate statistical solutions for modern complex data. This project aims to develop novel methods for reliable and accurate statistical modelling with modern, complex correlated and error-prone data. The project expects to make significant strides towards future-proofing statistical data analysis, equipping practitioners with a suite of robust and computationally efficient methods which provide confidence in the stability and reproducibility of results obtained, while offering guar ....Reliable and accurate statistical solutions for modern complex data. This project aims to develop novel methods for reliable and accurate statistical modelling with modern, complex correlated and error-prone data. The project expects to make significant strides towards future-proofing statistical data analysis, equipping practitioners with a suite of robust and computationally efficient methods which provide confidence in the stability and reproducibility of results obtained, while offering guarantees on their transferability over a range of populations. This will provide important benefits as they are applied in predicting endangered marine species for fisheries conservation, and in enhancing our national understanding of the relationship between education achievement and financial success. Read moreRead less
Surveillance and sampling to maintain absence of pests and diseases. This project aims to develop empirically validated statistical and mathematical methods for industry and government to deliver more efficient biosecurity surveillance programs. The project endeavours to enhance biosecurity at the border and within Australia, while minimising the costs and burden of testing. Expected project outcomes include effective surveillance and sampling for high-priority threats, accessible software for d ....Surveillance and sampling to maintain absence of pests and diseases. This project aims to develop empirically validated statistical and mathematical methods for industry and government to deliver more efficient biosecurity surveillance programs. The project endeavours to enhance biosecurity at the border and within Australia, while minimising the costs and burden of testing. Expected project outcomes include effective surveillance and sampling for high-priority threats, accessible software for decision-makers, and generalisable approaches to address rapidly increasing biosecurity risks. Significant benefits include maintaining absence of key pathogens and pests in Australia.Read moreRead less
Feature Learning for High-dimensional Functional Time Series. This project aims to develop new methods and theories for common features on high-dimensional functional time series observed in empirical applications. The significance includes addressing a key gap in adaptive and efficient feature learning, improving forecasting accuracy and understanding forecasting-driven factors comprehensively for empirical data. Expected outcomes involve advances in big data theory and easy-to-implement algori ....Feature Learning for High-dimensional Functional Time Series. This project aims to develop new methods and theories for common features on high-dimensional functional time series observed in empirical applications. The significance includes addressing a key gap in adaptive and efficient feature learning, improving forecasting accuracy and understanding forecasting-driven factors comprehensively for empirical data. Expected outcomes involve advances in big data theory and easy-to-implement algorithms for applied researchers. This project benefits not only advanced manufacturing by finding optimal stopping time for wood panel compression, but also superior forecasting for mortality in demography, climate data in environmental science, asset returns in finance, and electricity consumption in economics. Read moreRead less
Modern statistical methods for clustering community ecology data. This project will develop statistical methods and software for clustering community ecology data, and use them to analyse systematic survey and citizen science program data collected along the Great Barrier Reef. By doing so, the project will address the dearth of statistical classification techniques for high-dimensional, multi-response data with complex relationships. When the resultant clustering methods are used to construct b ....Modern statistical methods for clustering community ecology data. This project will develop statistical methods and software for clustering community ecology data, and use them to analyse systematic survey and citizen science program data collected along the Great Barrier Reef. By doing so, the project will address the dearth of statistical classification techniques for high-dimensional, multi-response data with complex relationships. When the resultant clustering methods are used to construct bioregions and characterise species’ environmental responses, they should significantly enhance evaluations of the impact of human activity and environmental change on coral diversity. Ultimately, these evaluations can underpin future decisions in the conservation and management of the Great Barrier Reef.Read moreRead less