New nonparametric statistical methods for imperfectly observed data. Statistical science today is facing the challenge of having to answer questions about data that are more complex than ever before. Some of the major difficulties are caused by the lack of direct access to quantities of interest, and the more intricate structure of the available data. Motivated by applications in areas such as cancer and genetic studies, infectious disease, environmental pollution, and public health and nutriti ....New nonparametric statistical methods for imperfectly observed data. Statistical science today is facing the challenge of having to answer questions about data that are more complex than ever before. Some of the major difficulties are caused by the lack of direct access to quantities of interest, and the more intricate structure of the available data. Motivated by applications in areas such as cancer and genetic studies, infectious disease, environmental pollution, and public health and nutrition, this project aims to develop novel and highly effective statistical methodology for solving contemporary problems involving new types of imperfectly observed data. The expected outcomes will solve frontier problems, where information can only be accessed through sophisticated computer intensive methods.Read moreRead less
Statistical challenges involving indirect data. This project aims to develop statistical methodology for solving contemporary problems involving indirectly observed data whose complexity is exacerbated by factors such as incompleteness or episodic availability. Modern statistics find it difficult to analyse complex data which contain important information only in an indirect way, such as data measured with noise or aggregated data. This project considers both finite dimensional data and function ....Statistical challenges involving indirect data. This project aims to develop statistical methodology for solving contemporary problems involving indirectly observed data whose complexity is exacerbated by factors such as incompleteness or episodic availability. Modern statistics find it difficult to analyse complex data which contain important information only in an indirect way, such as data measured with noise or aggregated data. This project considers both finite dimensional data and functional data. The expected methodology will be able to solve frontier problems, where only sophisticated methods can access information. This is expected to benefit brain studies, economics, infectious disease, nutrition and public health.Read moreRead less