Statistical and computational methods using a multiscale approach for protein identification and quantification. Proteins are critically important in the onset and ongoing illness associated with disease. Key proteins may serve as markers to diagnose or predict the course of a disease, or even become the target of pharmaceuticals. Accurate, efficient and robust algorithms are a critical component in protein identification. This research provides novel statistical algorithms for protein identific ....Statistical and computational methods using a multiscale approach for protein identification and quantification. Proteins are critically important in the onset and ongoing illness associated with disease. Key proteins may serve as markers to diagnose or predict the course of a disease, or even become the target of pharmaceuticals. Accurate, efficient and robust algorithms are a critical component in protein identification. This research provides novel statistical algorithms for protein identification using multiscale analysis techniques. Their applications in the bio-medical field will enable Australian and international researchers to identify key proteins more accurately, than current methods, leading to improve health, medical, and biological research outcomes.Read moreRead less
New statistical methods for identifying micro-ribonucleic acid (miRNA) regulatory networks. Understanding gene regulatory networks is critical in the understanding of fundamental biological systems. These networks have important implications for the discovery of fundamental mechanisms relating to the diagnosis and management of many illnesses. This research will provide new statistical methods to identify regulatory micro-ribonucleic acid modules and to understand their relationship in gene regu ....New statistical methods for identifying micro-ribonucleic acid (miRNA) regulatory networks. Understanding gene regulatory networks is critical in the understanding of fundamental biological systems. These networks have important implications for the discovery of fundamental mechanisms relating to the diagnosis and management of many illnesses. This research will provide new statistical methods to identify regulatory micro-ribonucleic acid modules and to understand their relationship in gene regulatory networks through multiple covariance estimation and multivariate classification techniques. My results should enable researchers to better understand the regulation underlying biological systems, leading to improved human health, medical and biological research outcomes.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130101670
Funder
Australian Research Council
Funding Amount
$370,410.00
Summary
Scalable Bayesian model selection for massive data sets. This project will develop highly innovative, efficient and ultimately effective methodology for Bayesian model selection for large-scale problems which commonly arise in biostatistics and bioinformatics. The resulting methodology will dramatically reduce the duration of analyses in these areas from days or weeks to minutes or hours.
Discovery Early Career Researcher Award - Grant ID: DE170100748
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Statistical tools for assessing effects of environmental change. This project aims to develop statistical tools for improving prediction of environmental exceedances, such as atmospheric carbon dioxide sources and sinks. Predicting extreme environmental conditions or events is crucial for effective environmental decision-making and management. The project will develop the tools using statistical inference based on a statistical model that combines predictions from related scientific models. In t ....Statistical tools for assessing effects of environmental change. This project aims to develop statistical tools for improving prediction of environmental exceedances, such as atmospheric carbon dioxide sources and sinks. Predicting extreme environmental conditions or events is crucial for effective environmental decision-making and management. The project will develop the tools using statistical inference based on a statistical model that combines predictions from related scientific models. In the case of carbon dioxide, improving prediction reliability by reducing bias and uncertainty whilst accounting for model-based dependence is an important step toward mitigating carbon dioxide sources and protecting carbon dioxide sinks. This capability is crucial for adaptive planning and a resilient society.Read moreRead less
Spatio-Temporal Statistics and its Application to Remote Sensing. By their very nature, environmental processes involve strong spatial and temporal variability. Inferring cause-effect relationships requires the incorporation of spatial and temporal dependence in the statistical models. The aims of this project are to develop mass-balanced hierarchical spatio-temporal statistical models, new loss functions that are relevant to multivariate processes, and optimal estimators obtained from the hiera ....Spatio-Temporal Statistics and its Application to Remote Sensing. By their very nature, environmental processes involve strong spatial and temporal variability. Inferring cause-effect relationships requires the incorporation of spatial and temporal dependence in the statistical models. The aims of this project are to develop mass-balanced hierarchical spatio-temporal statistical models, new loss functions that are relevant to multivariate processes, and optimal estimators obtained from the hierarchical model's predictive distribution. These methodologies are intended to be applied to the estimation of near-surface fluxes of atmospheric carbon dioxide, using massive remote sensing datasets from satellites and other data sources.Read moreRead less
Semiparametric Regression for Streaming Data. Semiparametric regression converts large and complex data-sets into interpretable summaries from which sound decisions can be made. This project tackles semiparametric regression analysis of streaming data - where the data are so voluminous that they may not be storable in standard computer memory and therefore need to be processed rapidly on arrival and then discarded. Effective solutions necessitate a rethinking of semi-parametric regression and ne ....Semiparametric Regression for Streaming Data. Semiparametric regression converts large and complex data-sets into interpretable summaries from which sound decisions can be made. This project tackles semiparametric regression analysis of streaming data - where the data are so voluminous that they may not be storable in standard computer memory and therefore need to be processed rapidly on arrival and then discarded. Effective solutions necessitate a rethinking of semi-parametric regression and new approaches will be developed. The project will also develop novel theory and methodology for robotics applications. It will allow analysis of streaming and massive data sets that would not be possible using currently available methods, opening up new applications.Read moreRead less
Fast approximate inference methods: new algorithms, applications and theory. This project aims to develop new algorithms and theory for fast approximate inference and lay down infrastructure to aid future extensions. Fast approximate inference methods are a principled and extensible means of fitting large and complex statistical models to big data sets. They come into their own in applications where speed is paramount and traditional approaches are not feasible. The project aims to lead to prac ....Fast approximate inference methods: new algorithms, applications and theory. This project aims to develop new algorithms and theory for fast approximate inference and lay down infrastructure to aid future extensions. Fast approximate inference methods are a principled and extensible means of fitting large and complex statistical models to big data sets. They come into their own in applications where speed is paramount and traditional approaches are not feasible. The project aims to lead to practical outcomes from better business decision-making for insurance data warehouses, to improved medical imaging technology.Read moreRead less
Reliable and accurate statistical solutions for modern complex data. This project aims to develop novel methods for reliable and accurate statistical modelling with modern, complex correlated and error-prone data. The project expects to make significant strides towards future-proofing statistical data analysis, equipping practitioners with a suite of robust and computationally efficient methods which provide confidence in the stability and reproducibility of results obtained, while offering guar ....Reliable and accurate statistical solutions for modern complex data. This project aims to develop novel methods for reliable and accurate statistical modelling with modern, complex correlated and error-prone data. The project expects to make significant strides towards future-proofing statistical data analysis, equipping practitioners with a suite of robust and computationally efficient methods which provide confidence in the stability and reproducibility of results obtained, while offering guarantees on their transferability over a range of populations. This will provide important benefits as they are applied in predicting endangered marine species for fisheries conservation, and in enhancing our national understanding of the relationship between education achievement and financial success. 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
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