Large dynamic time-varying models for structural macroeconomic inference. This project aims to broaden the range of macroeconomic models that have an integrated capacity for both greater realism and efficiency in analysis. This approach will be applied to two contexts at the forefront of current macroeconomic research, the effects of noisy productivity signals on business cycles and the effects of fiscal policy shocks. Flexible macro-econometric models underpin accurate inference by economists ....Large dynamic time-varying models for structural macroeconomic inference. This project aims to broaden the range of macroeconomic models that have an integrated capacity for both greater realism and efficiency in analysis. This approach will be applied to two contexts at the forefront of current macroeconomic research, the effects of noisy productivity signals on business cycles and the effects of fiscal policy shocks. Flexible macro-econometric models underpin accurate inference by economists and policymakers and the project outputs should provide widespread and significant benefits by improving policy and boosting Australia’s comparative advantage.Read moreRead less
Bayesian inference for psychological theories with intractable likelihood. This project pursues breakthroughs which allow important questions of basic and applied science to be addressed using mathematical theories from cognitive psychology. Advances are made through an interdisciplinary effort, combining recent developments in econometric and statistical methods and cognitive science. The outcomes will advance knowledge and open up new avenues for applied research in important aspects of psych .... Bayesian inference for psychological theories with intractable likelihood. This project pursues breakthroughs which allow important questions of basic and applied science to be addressed using mathematical theories from cognitive psychology. Advances are made through an interdisciplinary effort, combining recent developments in econometric and statistical methods and cognitive science. The outcomes will advance knowledge and open up new avenues for applied research in important aspects of psychology. This research will result in new methods available to the wider scientific community which open up new horizons for understanding basic cognition, and human behavior in many domains. Read moreRead less
Flexible models and methods for cognitive model-based decision-making. This project aims to apply mathematical decision models to important questions of basic and applied science. Advances will be pursued through an interdisciplinary effort, combining recent developments in econometric and statistical methods, cognitive science and computing. The expected outcomes will bring a proven and powerful approach to a new range of questions investigating psychological aspects of choices about health c ....Flexible models and methods for cognitive model-based decision-making. This project aims to apply mathematical decision models to important questions of basic and applied science. Advances will be pursued through an interdisciplinary effort, combining recent developments in econometric and statistical methods, cognitive science and computing. The expected outcomes will bring a proven and powerful approach to a new range of questions investigating psychological aspects of choices about health care and consumer purchases. This project will provide significant benefits to the wider scientific community to understand basic cognition, and human behaviour in many domains.Read moreRead less
Statistical Analysis of State-Dependent Government Spending Multipliers. This project aims to provide a new statistical analysis of the government spending multiplier by acknowledging that government spending is the sum of sectoral spending which has heterogeneous effects on the economy. An added complication is that the multiplier can also be state-dependent, meaning that its magnitude can differ across recessions and expansions. Expected outcomes of this project include a better understanding ....Statistical Analysis of State-Dependent Government Spending Multipliers. This project aims to provide a new statistical analysis of the government spending multiplier by acknowledging that government spending is the sum of sectoral spending which has heterogeneous effects on the economy. An added complication is that the multiplier can also be state-dependent, meaning that its magnitude can differ across recessions and expansions. Expected outcomes of this project include a better understanding of the components of the multiplier by novel decomposition and the development of a new statistical test for the state-dependency of the multiplier. This should provide significant benefits to researchers by bringing in new tools and insights and to policymakers by providing timely guidance on fiscal policies.Read moreRead less
New methods in spectral geometry. This project aims to use methods from mathematical scattering theory to resolve problems in the spectral analysis and index theory of differential operators. Both areas underpin the theoretical understanding of physical materials at micro length scales where quantum phenomena dominate. The project will develop new mathematical results in spectral analysis and geometry, and apply its results to theoretical models of quantum phenomena whose spectral properties are ....New methods in spectral geometry. This project aims to use methods from mathematical scattering theory to resolve problems in the spectral analysis and index theory of differential operators. Both areas underpin the theoretical understanding of physical materials at micro length scales where quantum phenomena dominate. The project will develop new mathematical results in spectral analysis and geometry, and apply its results to theoretical models of quantum phenomena whose spectral properties are at the limit of the range of mathematical techniques. Solving these problems is expected to influence non-commutative analysis.Read moreRead less
Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, ....Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, statistics and different ways of aggregating information. By building a more complete theoretical map it is expected that new machine learning methods will be developed, but more importantly that machine learning will be able to be better integrated into larger socio-technical systems.Read moreRead less
Principled statistical methods for high-dimensional correlation networks. This project aims to develop a novel and principled approach for building correlation networks. Correlation networks aim to identify the most significant associations present in modern massive datasets, and have numerous applications, ranging from the biomedical and environmental sciences to the social sciences. Nodes of such networks represent features, and edges represent associations, or the lack thereof. Current method ....Principled statistical methods for high-dimensional correlation networks. This project aims to develop a novel and principled approach for building correlation networks. Correlation networks aim to identify the most significant associations present in modern massive datasets, and have numerous applications, ranging from the biomedical and environmental sciences to the social sciences. Nodes of such networks represent features, and edges represent associations, or the lack thereof. Current methods are not readily scalable to modern ultra-high dimensional settings, and do not account for uncertainty in the estimated associations. This project will develop a principled, highly scalable methodology for building such networks, which incorporates uncertainty quantification. Emphasis is placed on modern ultra-high dimensional settings in which differentiating a true correlation from a spurious one is a notoriously difficult task.Read moreRead less
Measuring uncertainty in global housing markets and its risk to Australia. This project aims to develop and construct a measure of systemic risk for the national real-estate markets in Australia, and its main trading partners, namely China, Japan, New Zealand, United Kingdom and United States of America. Recently developed methodology will be used to investigate how real estate risks migrate across these countries over time, and during periods of financial turbulence. This methodology is intende ....Measuring uncertainty in global housing markets and its risk to Australia. This project aims to develop and construct a measure of systemic risk for the national real-estate markets in Australia, and its main trading partners, namely China, Japan, New Zealand, United Kingdom and United States of America. Recently developed methodology will be used to investigate how real estate risks migrate across these countries over time, and during periods of financial turbulence. This methodology is intended to be employed as part of a market stability surveillance program and for assessing the impact of real-estate risk on the overall economy. Early detection of the onset of future housing bubble collapses would be of significant benefit to policy makers, Australia’s trading partners, the real estate industry and ultimately home buyers.Read moreRead less
Advanced Bayesian Inversion Algorithms for Wave Propagation. This project aims to improve algorithms for detecting hidden items by developing new computational mathematical techniques capable of reconstructing the shape and location of objects using electromagnetic waves. This project expects to generate new knowledge in the areas of Bayesian Inversion and computational wave propagation. Expected outcomes of this project are algorithms that can be developed for use in nonintrusive radio wave sec ....Advanced Bayesian Inversion Algorithms for Wave Propagation. This project aims to improve algorithms for detecting hidden items by developing new computational mathematical techniques capable of reconstructing the shape and location of objects using electromagnetic waves. This project expects to generate new knowledge in the areas of Bayesian Inversion and computational wave propagation. Expected outcomes of this project are algorithms that can be developed for use in nonintrusive radio wave security scanners. This should provide benefits such as the capability to scan a crowd without a checkpoint, which will have the potential to improve security in public places.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180101252
Funder
Australian Research Council
Funding Amount
$343,450.00
Summary
Statistical theory and algorithms for joint inference of complex networks. This project aims to address the challenges in jointly modelling complex networks by applying an integrated approach encompassing statistical theory, computation, and applications. The project expects to contribute to core statistical methodology development for complex inference and generate new knowledge in the fields of genomics, neuroscience, and social science through in-depth analyses of large-scale multilayered net ....Statistical theory and algorithms for joint inference of complex networks. This project aims to address the challenges in jointly modelling complex networks by applying an integrated approach encompassing statistical theory, computation, and applications. The project expects to contribute to core statistical methodology development for complex inference and generate new knowledge in the fields of genomics, neuroscience, and social science through in-depth analyses of large-scale multilayered network data. Expected outcomes include enhanced theoretical and computational frameworks for probabilistic network models to better utilise the power of multiple observations. This should foster international and interdisciplinary collaborations and add significant value to the rapidly progressing field of networks research.Read moreRead less