An integrated toolkit for high-dimensional complex and time series data analysis
Fang Han · Research outreach · 2019
A dvances in technology and the advent of big data mean that huge sets of high-dimensional, unstructured data are becoming common place; as is the need to collect, store and process them.For example, stock market analysis, genetic testing, and magnetic resonance imaging all produce massive amounts of high-dimensional data.These data sets are too large and too complex to be dealt with using traditional methods and conventional software packages often struggle to handle them. COMPLEX DATA CHALLENGEStatisticians are faced with new challenges from this complex data.These enormous quantities of very high-dimensional data can be skewed, exhibit nonlinear relationships and contain useless information or noise, preventing them from being analysed by traditional parametric or linear methods.Dr Fang Han, Assistant Professor of Statistics at the University of Washington, Seattle, is meeting this challenge head on.His research focuses on high-dimensional statistical theory and its application in order to resolve statistical problems in the fields of economics, finance, and science.He highlights a requirement for statistical methods that can be scaled up to handling large datasets and acknowledges that while these methods have to be able to capture the subtleties of the particular area of interest, they also have to be able to cope with different modelling assumptions and data contamination.He also draws attention to the development of statistical theory and methodology generally lagging behind the development of new technologies.