Mechanism design for next generation random access wireless protocols. Australia is well placed to take the lead in replacing carbon-intensive travel by teleconferencing, because of its isolation and geographic dispersion. Because these large distances introduce inevitable delays, it is important that the network itself add as little delay as possible for such real-time services. Our novel and practical resource allocation scheme will enable Australians (including Australian industries and rural ....Mechanism design for next generation random access wireless protocols. Australia is well placed to take the lead in replacing carbon-intensive travel by teleconferencing, because of its isolation and geographic dispersion. Because these large distances introduce inevitable delays, it is important that the network itself add as little delay as possible for such real-time services. Our novel and practical resource allocation scheme will enable Australians (including Australian industries and rural communities) to receive better service at lower cost. This project will put Australia on the international stage as a leading contributor to wireless Internet technology. We will provide training for PhD students and postdoctoral fellows in this important area.Read moreRead less
ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights. In today's world, massive amounts of data in a variety of forms are collected daily from a multitude of sources. Many of the resulting data sets have the potential to make vital contributions to society, business and government, as well as impact on international developments, but are so large or complex that they are difficult to process and analyse using traditional tools. The aim of this ....ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights. In today's world, massive amounts of data in a variety of forms are collected daily from a multitude of sources. Many of the resulting data sets have the potential to make vital contributions to society, business and government, as well as impact on international developments, but are so large or complex that they are difficult to process and analyse using traditional tools. The aim of this Centre is to create innovative mathematical and statistical models that can uncover the knowledge concealed within the size and complexity of these big data sets, with a focus on using the models to deliver insight into problems vital to the Centre's Collaborative Domains: Healthy People, Sustainable Environments and Prosperous Societies.Read moreRead less
The estimation of genotype-phenotype relationships from family data and of animal abundance from capture-recapture data with frequent capture occasions: A semiparametric approach. Semiparametric statistical methods allow researchers to only model those features of their data that are of interest, but still allow standard statistical inferences to be made about these features. The aim here is to develop non standard applications of semiparametric statistical methods in the estimation of genotype ....The estimation of genotype-phenotype relationships from family data and of animal abundance from capture-recapture data with frequent capture occasions: A semiparametric approach. Semiparametric statistical methods allow researchers to only model those features of their data that are of interest, but still allow standard statistical inferences to be made about these features. The aim here is to develop non standard applications of semiparametric statistical methods in the estimation of genotype-phenotype relationships from family data and the estimation of animal abundance from capture-recapture data. The methods will be applied to real data and their theoretical properties developed. The practical significance of the project is the flexible new statistical methods that will become available to researchers. The theoretical significance will be the insights into semiparametric methods gained by developing these nonstandard applications. The expected outcomes are the new statistical procedures and the resulting theoretical insights into semiparametric statistics.Read moreRead less
Advanced matrix-analytic methods with applications. Over the last twenty-five years, matrix-analytic methods have proved to be very successful in formulating and analysing certain classes of stochastic models. Motivated by applications, this project will investigate more advanced matrix-analytic methods than have hitherto been studied.
Stein's method for probability approximation. Data of counts in time, such as incoming calls in telecommunications and the clusters of palindromes in a family of herpes-virus genomes, arise in an extraordinarily diverse range of fields from science to business. These problems can be modelled by sums of random variables taking values 0 and 1 in probability theory, thus permitting approximate calculations which are often good enough in practice. This project will obtain such approximate solutions ....Stein's method for probability approximation. Data of counts in time, such as incoming calls in telecommunications and the clusters of palindromes in a family of herpes-virus genomes, arise in an extraordinarily diverse range of fields from science to business. These problems can be modelled by sums of random variables taking values 0 and 1 in probability theory, thus permitting approximate calculations which are often good enough in practice. This project will obtain such approximate solutions and estimate the errors involved. Applications include analysis of data in insurance, finance, flood prediction in hydrology.Read moreRead less
Dynamic prediction models in Australian rules football using real time performance statistics. The study is a collaborative venture with Champion Data, the Australian leader in the collection and transmission of real time sporting data, and official provider of the Australian Football League (AFL) statistics. The aim is to develop a real time on line predictive model for AFL football. The model will use the statistics Champion Data collect as the match progresses as inputs to continually updat ....Dynamic prediction models in Australian rules football using real time performance statistics. The study is a collaborative venture with Champion Data, the Australian leader in the collection and transmission of real time sporting data, and official provider of the Australian Football League (AFL) statistics. The aim is to develop a real time on line predictive model for AFL football. The model will use the statistics Champion Data collect as the match progresses as inputs to continually update estimates of the probabilities of various outcomes of interest such as the winner of the match and the margin of victory. The project will assist Champion in their strategic aim to provide an on line form guide.Read moreRead less
Random walks with long memory. This project aims to study novel random walk models with long memory, including systems of multiple random walkers that interact through their environment. This would provide a mathematical understanding of phenomena such as aggregation in colonies of bacteria, and ant colony optimisation algorithms. The project aims to produce highly cited publications, and to train future researchers.
Discovery Early Career Researcher Award - Grant ID: DE200101467
Funder
Australian Research Council
Funding Amount
$419,778.00
Summary
The geometric structure of spatial noise. Spatial noise is ubiquitous in nature and science: as interference in medical imaging, in oceanography, in the modelling of telecommunication networks etc. Despite this diversity of sources, spatial noise can be studied in a unified way by considering mathematical models that capture its essential features. This project aims to study spatial noise by analysing its geometric structure, for instance by considering the number of contour lines of the noise, ....The geometric structure of spatial noise. Spatial noise is ubiquitous in nature and science: as interference in medical imaging, in oceanography, in the modelling of telecommunication networks etc. Despite this diversity of sources, spatial noise can be studied in a unified way by considering mathematical models that capture its essential features. This project aims to study spatial noise by analysing its geometric structure, for instance by considering the number of contour lines of the noise, and the way these lines connect different regions of space. The project further aims to apply this analysis to construct statistical tests that can distinguish different classes of spatial noise, with potential applications across all of the disciplines mentioned above.Read moreRead less
Censored Regression Techniques for Credit Scoring. This project will apply censored regression techniques to a loans database from the industry partner, the ANZ bank. We will accurately estimate the actual time to loan repayment, rather than simply the risk of default. In a novel approach for credit scoring we will build a model using current, right-censored, rather than historic data, incorporating loans that are not yet repaid but are underway and clearly have a length of loan longer than obse ....Censored Regression Techniques for Credit Scoring. This project will apply censored regression techniques to a loans database from the industry partner, the ANZ bank. We will accurately estimate the actual time to loan repayment, rather than simply the risk of default. In a novel approach for credit scoring we will build a model using current, right-censored, rather than historic data, incorporating loans that are not yet repaid but are underway and clearly have a length of loan longer than observed. This approach has the immense advantage of being able to reflect contemporary borrowing patterns in the model, rather than relying on historic trends.
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Modelling patient flows through hospitals: optimizing effective use of resources. Hospitals are complex, dynamic systems confronted by increased demand in the face of shrinking real capacity. Managing such systems is currently undertaken with sub-optimal analytical support, particularly when demand and capacity are changing and resources must be manipulated to respond to such changes. In this project, the investigators will apply a mathematical modelling approach to the analysis of hospital pati ....Modelling patient flows through hospitals: optimizing effective use of resources. Hospitals are complex, dynamic systems confronted by increased demand in the face of shrinking real capacity. Managing such systems is currently undertaken with sub-optimal analytical support, particularly when demand and capacity are changing and resources must be manipulated to respond to such changes. In this project, the investigators will apply a mathematical modelling approach to the analysis of hospital patient flows. Furthermore, they will employ statistical process control methodologies to the problem of recognising and responding to changes in the flows, so that performance objectives are met. In doing this, they will give health service managers and clinicians a significant advantage in deciding how best to manage a constrained resource to maximize access, throughput and patient outcomes.Read moreRead less