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
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
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