Advanced Mixture Models for the Analysis of Modern-Day Data. Extracting key information from huge data sets is critical to the scientific successes of the future. This project will develop novel mixture models that can be used directly to analyse complex and high-dimensional data sets that may consist of thousands of variables observed on only a limited number of entities. In order to handle the challenging problems arising in the latter situation. This project develops mixtures of factor models ....Advanced Mixture Models for the Analysis of Modern-Day Data. Extracting key information from huge data sets is critical to the scientific successes of the future. This project will develop novel mixture models that can be used directly to analyse complex and high-dimensional data sets that may consist of thousands of variables observed on only a limited number of entities. In order to handle the challenging problems arising in the latter situation. This project develops mixtures of factor models with options for skew distributions that can be used to effectively analyse such data. Key applications include the domains of bioinformatics, biostatistics, business, data mining, economics, finance, image analysis, marketing, and personalised medicine, among many others.Read moreRead less
Joint clustering and matching of multivariate samples across objects. The project will provide a novel and very effective approach to the clustering of multivariate samples on objects, say patients, that automatically matches the sample clusters across the objects. A key application is the matching of biologically relevant cell subtypes across patients for use in the study and the clinical diagnosis and prognosis of cancer.
Expanding the role of mixture models in statistical analyses of big data. This project aims to develop theoretical procedures to scale inference and learning algorithms to analyse big data sets. It will develop analytic tools and algorithms to analyse big data sets which classical methods of inference cannot analyse directly due to the data’s complexity or size. This will accelerate the progress of scientific discovery and innovation, leading, for example, to new fields of inquiry; to an increas ....Expanding the role of mixture models in statistical analyses of big data. This project aims to develop theoretical procedures to scale inference and learning algorithms to analyse big data sets. It will develop analytic tools and algorithms to analyse big data sets which classical methods of inference cannot analyse directly due to the data’s complexity or size. This will accelerate the progress of scientific discovery and innovation, leading, for example, to new fields of inquiry; to an increase in understanding from studies on human and social processes and interactions; and to the promotion of economic growth and improved health and quality of life. Such applications should lead to breakthrough discoveries and innovation in science, engineering, medicine, commerce, education and national security.Read moreRead less
A new approach to fast matrix factorization for the statistical analysis of high-dimensional data. Some form of dimension reduction is essential in order to extract meaningful information from huge data sets. For this purpose we provide a novel and very fast approach to the factorization of the data matrix. It has wide applicability for improving the quality and validity of research in science and medicine and in most industries in Australia.
Large-Scale Statistical Inference: Multiple Testing. Multiple testing procedures are among the most important statistical tools for the analysis of modern data. This project aims to develop new methods for providing more powerful simultaneous tests while controlling the proportion of false positive conclusions. They are proposed to be derived by the novel pooling of information in individual attribute based contrasts to produce a Weighted Individual attribute-Specific Contrast (WISC) based stati ....Large-Scale Statistical Inference: Multiple Testing. Multiple testing procedures are among the most important statistical tools for the analysis of modern data. This project aims to develop new methods for providing more powerful simultaneous tests while controlling the proportion of false positive conclusions. They are proposed to be derived by the novel pooling of information in individual attribute based contrasts to produce a Weighted Individual attribute-Specific Contrast (WISC) based statistic. They will also exploit contextual information. They are expected to be of direct application to the problem of testing for no differences between two or more classes, as in the detection of differential expression in bioinformatics. Other key applications are expected to include biomedicine, economics, finance, genetics, and neuroscience.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE170101134
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Feasible algorithms for big inference. This project aims to develop algorithms for computationally-intensive statistical tools to analyse Big Data. Big Data is ubiquitous in science, engineering, industry and finance, but needs special machine learning to conduct correct inferential analysis. Computational bottlenecks make many tried-and-true tools of statistical inference inadequate. This project will develop tools including false discovery rate control, heteroscedastic and robust regression an ....Feasible algorithms for big inference. This project aims to develop algorithms for computationally-intensive statistical tools to analyse Big Data. Big Data is ubiquitous in science, engineering, industry and finance, but needs special machine learning to conduct correct inferential analysis. Computational bottlenecks make many tried-and-true tools of statistical inference inadequate. This project will develop tools including false discovery rate control, heteroscedastic and robust regression and mixture models, via Big Data-appropriate optimisation and composite-likelihood estimation. It will make open, well-documented, and accessible software available for the scalable and distributable analysis of Big Data. The expected outcome is a suite of scalable algorithms to analyse Big Data.Read moreRead less
Statistical methodology for events on a network, with application to road safety. This project develops new methods to analyse road traffic accident rates, aiming to identify accident black spots and to develop an evidence base for future road design and road safety management. These methods can be applied to other types of events on a network of roads, railways, rivers, electrical wires, communication networks or airline routes.
Rating and ranking sports players and teams using minimum message length. All sorts of games and sports could use better systems for rating and ranking teams. This is as true in sports-mad Australia as any other country. Improved and more accessible rating systems across a variety of activities should encourage the general public to take a greater interest in the mathematics, statistics, information theory and machine learning behind the systems. With Cadability as our Australia-based interna ....Rating and ranking sports players and teams using minimum message length. All sorts of games and sports could use better systems for rating and ranking teams. This is as true in sports-mad Australia as any other country. Improved and more accessible rating systems across a variety of activities should encourage the general public to take a greater interest in the mathematics, statistics, information theory and machine learning behind the systems. With Cadability as our Australia-based international industry partner, the global use of these systems will be to Australia's economic advantage. Having a more accurate rating system which is wider-reaching both in the number of sports and games and the number of participants per sport and game should also encourage greater participation from the general public.Read moreRead less
Machine learning in adversarial environments. Machine learning underpins the technologies driving the economies of both Silicon Valley and Wall Street, from web search and ad placement, to stock predictions and efforts in fighting cybercrime. This project aims to answer the question: How can machines learn from data when contributors act maliciously for personal gain?
Complex data, model selection and bootstrap inference. The project will provide new statistical methods and associated software for the analysis and modelling of complex data, as well as quality research training. This project will benefit researchers in statistics and users of statistics who encounter the complex data considered in this project and who need to model and make inferences from these data. Since these kinds of data arise in many areas (such as medicine, genetics, chemistry etc), ....Complex data, model selection and bootstrap inference. The project will provide new statistical methods and associated software for the analysis and modelling of complex data, as well as quality research training. This project will benefit researchers in statistics and users of statistics who encounter the complex data considered in this project and who need to model and make inferences from these data. Since these kinds of data arise in many areas (such as medicine, genetics, chemistry etc), Australia and Australian industry will ultimately benefit from the proposed research. The strengthening of international link and the training of highly trained research scientists in an area of national importance will also benefit Australia.Read moreRead less