Robust methods for heteroscedastic regression models for time series. What is the variability of the exchange rate of the Euro to the Australian dollar? Can the use of the electrocardiogram of a patient be improved as a diagnostic tool for heart disease? A well-known limitation of the existing statistical methods for answering these types of questions is that a small proportion of extreme observations have the potential to lead to results that are more in agreement with the outliers than with bu ....Robust methods for heteroscedastic regression models for time series. What is the variability of the exchange rate of the Euro to the Australian dollar? Can the use of the electrocardiogram of a patient be improved as a diagnostic tool for heart disease? A well-known limitation of the existing statistical methods for answering these types of questions is that a small proportion of extreme observations have the potential to lead to results that are more in agreement with the outliers than with bulk of the data. As a consequence, the statistical analyses may lead to wrong conclusions. This project aims to develop new methodologies to solve this problem for a large class of studies. Applications to stock market risk, exchange rate, and diagnosis of heart diseases will illustrate the new methods.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101070
Funder
Australian Research Council
Funding Amount
$376,496.00
Summary
Consequences of Model Misspecification in Approximate Bayesian Computation. In almost any empirical application, the model the analyst is working with constitutes a misspecified description of the true process that has generated the data. While the method of Approximate Bayesian computation (ABC) is now a staple in the toolkit of the applied modeller, the impact of misspecification in ABC is unknown. This project aims to undertake a rigorous study into the behaviour of ABC under model misspecifi ....Consequences of Model Misspecification in Approximate Bayesian Computation. In almost any empirical application, the model the analyst is working with constitutes a misspecified description of the true process that has generated the data. While the method of Approximate Bayesian computation (ABC) is now a staple in the toolkit of the applied modeller, the impact of misspecification in ABC is unknown. This project aims to undertake a rigorous study into the behaviour of ABC under model misspecification. Expected outcomes include new theoretical results for ABC under misspecification and new methods capable of detecting/mitigating model misspecification. This project will provide significant benefits in all spheres where reliable, robust statistical inference methods are required in order to make reliable decisions.
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Predicting the value and use of urban land. This project aims to develop a comprehensive, robust and user-friendly set of modelling tools to predict land values more accurately. Accurate predictions of land values reduce state government revenue risks and improve resource management. The expected outcome of this project is the development of modelling tools which, can be used to study land use allocation, infrastructure delivery and government taxation revenue. This should provide significant be ....Predicting the value and use of urban land. This project aims to develop a comprehensive, robust and user-friendly set of modelling tools to predict land values more accurately. Accurate predictions of land values reduce state government revenue risks and improve resource management. The expected outcome of this project is the development of modelling tools which, can be used to study land use allocation, infrastructure delivery and government taxation revenue. This should provide significant benefits such as the development of new econometric theory, advanced computational methods and evidence-based guidelines for policymakers.Read moreRead less
Loss-based Bayesian Prediction. This project proposes a new paradigm for prediction. Using state-of-the-art computational methods, the project aims to produce accurate, fit for purpose, predictions which, by design, reduce the loss incurred when the prediction is inaccurate. Theoretical validation of the new predictive method, without reliance on knowledge of the correct statistical model, is an expected outcome, as is an extensive numerical assessment of its performance in empirical settings. T ....Loss-based Bayesian Prediction. This project proposes a new paradigm for prediction. Using state-of-the-art computational methods, the project aims to produce accurate, fit for purpose, predictions which, by design, reduce the loss incurred when the prediction is inaccurate. Theoretical validation of the new predictive method, without reliance on knowledge of the correct statistical model, is an expected outcome, as is an extensive numerical assessment of its performance in empirical settings. The new paradigm should produce significant benefits for all fields in which the consequences of predictive inaccuracy are severe. Problems that lead to substantial economic, financial or environmental loss if predictions are incorrect will be given particular attention.Read moreRead less
New methods for modelling complex trends in climate and energy time series. The project aims to contribute to Australian and international efforts on emission control by advancing the methods for quantifying the relationships between energy production, emission and climate, and assessing the real and financial risks associated with changing the ways in which economies produce and use energy. The project is interdisciplinary and expects to develop new knowledge in the areas of energy and climate ....New methods for modelling complex trends in climate and energy time series. The project aims to contribute to Australian and international efforts on emission control by advancing the methods for quantifying the relationships between energy production, emission and climate, and assessing the real and financial risks associated with changing the ways in which economies produce and use energy. The project is interdisciplinary and expects to develop new knowledge in the areas of energy and climate econometrics. The anticipated outcomes of this project are new methods for modelling variables with complex trends, and an innovative data-driven approach for learning from policy experiences of other countries. This should provide significant benefits by enabling evidence-based policy making in the era of climate change. Read moreRead less