Stochastic analysis and the development and application of financial risk processes. Ensuring the stability of Australia's financial system requires an understanding of the complex financial instruments, strategies and technologies that have evolved in recent years. A strong well-integrated research effort in stochastic analysis with particular application to financial markets is fundamental for measuring and managing risk, to protect and preserve a well functioning system, and to inform policy ....Stochastic analysis and the development and application of financial risk processes. Ensuring the stability of Australia's financial system requires an understanding of the complex financial instruments, strategies and technologies that have evolved in recent years. A strong well-integrated research effort in stochastic analysis with particular application to financial markets is fundamental for measuring and managing risk, to protect and preserve a well functioning system, and to inform policy debate on financial strategies and insurance liabilities.
These challenges are global and require extensive international research collaboration and interaction. The present project will enhance Australia's contributions in this area and facilitate its global impact more than is possible through individual efforts.Read moreRead less
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
Financial Risk Processes: Stochastic and Statistical Models and their Applications. On the one hand, the misuse of complex financial instruments has contributed to recent major disasters in the Australian financial and insurance industries; on the other hand, great benefits can be obtained by correct use of these kinds of instruments, to share risk between markets and segments of markets. The overall research effort in Australia in these areas is relatively small. This project will target the de ....Financial Risk Processes: Stochastic and Statistical Models and their Applications. On the one hand, the misuse of complex financial instruments has contributed to recent major disasters in the Australian financial and insurance industries; on the other hand, great benefits can be obtained by correct use of these kinds of instruments, to share risk between markets and segments of markets. The overall research effort in Australia in these areas is relatively small. This project will target the development of cutting edge technologies underlying the use of financial derivatives, not presently studied in this country or elsewhere, by bringing together a variety of top level international researchers in an integrated effort to lift the Australian understanding and application of this methodology.Read moreRead less
Stochastic Analysis with a View to Applications in Financial Risk Processes. Recent decades have seen explosive growth in applications of probability theory and statistics to the modelling of risk in finance and insurance. An intensive theoretical investigation into passage time and other problems for Levy and other continuous time processes will be applied to financial risk analyses. Related investigations will involve perpetuities and stochastic volatility models for price series. Outcomes ....Stochastic Analysis with a View to Applications in Financial Risk Processes. Recent decades have seen explosive growth in applications of probability theory and statistics to the modelling of risk in finance and insurance. An intensive theoretical investigation into passage time and other problems for Levy and other continuous time processes will be applied to financial risk analyses. Related investigations will involve perpetuities and stochastic volatility models for price series. Outcomes will include the development of new theory in probability and statistics, the initiation and reinforcement of collaborative ties with major international research figures, and the fostering of contacts with the finance industry.Read moreRead less
Seasonal adjustment using disaggregated short time span data. Seasonally adjusted economic and social times series are vital information used by governments and businesses in decision making. This project will develop statistical methods to estimate and remove seasonal factors from economic and social time series using finely disaggregated data for a relatively small number of time periods. This will enable better and quicker estimation of seasonal factors when new series are introduced or there ....Seasonal adjustment using disaggregated short time span data. Seasonally adjusted economic and social times series are vital information used by governments and businesses in decision making. This project will develop statistical methods to estimate and remove seasonal factors from economic and social time series using finely disaggregated data for a relatively small number of time periods. This will enable better and quicker estimation of seasonal factors when new series are introduced or there a major changes to existing series, improving the analysis of such series and the decisions based on them.Read moreRead less
Increasing internet energy and cost efficiency by improving higher-layer protocols. Australians rely heavily on our telecommunications infrastructure due to our geographic dispersion. We are also very susceptible to climate change, given our reliance on agriculture. Information technology is consuming a rapidly increasing fraction of our power and our budget. This research will help to reverse both those trends, by finding novel and practical ways to use our infrastructure more efficiently, and ....Increasing internet energy and cost efficiency by improving higher-layer protocols. Australians rely heavily on our telecommunications infrastructure due to our geographic dispersion. We are also very susceptible to climate change, given our reliance on agriculture. Information technology is consuming a rapidly increasing fraction of our power and our budget. This research will help to reverse both those trends, by finding novel and practical ways to use our infrastructure more efficiently, and to minimise its energy use. This will enable the Australian telecommunications industry to provide better service (including to Australian industries and rural communities) at lower economic and environmental cost. This project will put Australia on the international stage as a leading contributor to energy-efficient internet technology.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
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