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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
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
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
Expenditure needs and drawdown of retirement savings during later life: how important are demographic factors and financial resources? Projections of expenditure patterns in retirement which allow for population heterogeneity will provide individuals with a better appreciation of their income needs and their savings requirements for a comfortable retirement. It will also enable financial institutions to develop products which better target retirees' needs over the course of retirement, and in ad ....Expenditure needs and drawdown of retirement savings during later life: how important are demographic factors and financial resources? Projections of expenditure patterns in retirement which allow for population heterogeneity will provide individuals with a better appreciation of their income needs and their savings requirements for a comfortable retirement. It will also enable financial institutions to develop products which better target retirees' needs over the course of retirement, and in addition it will enable improved assessment of aspects of Government income support policy. Specifically, understanding the complex interactions between private and public pensions, and concession card receipt upon expenditure behaviour, will enable more accurate costings of the public support of elderly families as Australia's population ages. Read moreRead less
Entropic Analysis of Financial Risk and Uncertainty. The recent financial crisis has shown that the financial markets are not as stable as expected, and are at risk from a lack of knowledge about new financial products and their risks. This research provides a framework to better measure and forecast financial risks by applying a set of techniques known collectively as entropic analysis as a novel way to measure the amount of information that can be extracted from historical data. The research w ....Entropic Analysis of Financial Risk and Uncertainty. The recent financial crisis has shown that the financial markets are not as stable as expected, and are at risk from a lack of knowledge about new financial products and their risks. This research provides a framework to better measure and forecast financial risks by applying a set of techniques known collectively as entropic analysis as a novel way to measure the amount of information that can be extracted from historical data. The research will facilitate the design of policies and regulations by regulatory authorities that need to evaluate new financial products, their associated risks and their impacts on the financial markets.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
Parallel and Distributed Machine Learning - Smart Data Analysis in the Multicore Era. In large data centres our research will lead to reduced energy consumption by using graphics cards which have a much better computation to power ratio than traditional processors. On desktop computers, it will make machine learning practical by enabling efficient algorithms for spam filtering and content analysis. On networked systems it will lead to distributed inference, caching and collaborative filtering ap ....Parallel and Distributed Machine Learning - Smart Data Analysis in the Multicore Era. In large data centres our research will lead to reduced energy consumption by using graphics cards which have a much better computation to power ratio than traditional processors. On desktop computers, it will make machine learning practical by enabling efficient algorithms for spam filtering and content analysis. On networked systems it will lead to distributed inference, caching and collaborative filtering applications which will both reduced the bandwidth required and make the internet safer for users. Finally, it will enable rapid deployment of sensor networks for monitoring and detection, such as for environmental monitoring and safeguarding Australia's borders.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