Qiwei’s recent research has mainly (but not exclusively) focused on analysing complex and high-dimensional time series in the sense that the observation recorded at each time is more complex than a single scalar or a short vector. Two examples under this framework are spatio-temporal data and dynamic network for which the observation at each time is a space or a network. Demand for analysing those data on an ever-increasing scale is a part of the real challenge underneath the buzzword BigData. The time series thinking and methodology can contribute in facing those challenges. An overarching aim has been to develop new tools which reduce the dimension and/or the complexity of time series by exploring latent low dimensional structures.
He has also published in the areas of nonlinear time series, functional time series, econometrics, extreme values of dependent data, non/semi-parametric estimation for conditional distributions, non/semi-parametric regression. you can find a full list of publications here.
Qiwei also enjoys collaborating with industry, which often leads to new, non-standard and challenging research problems.
With the increasing experience in research and, especially, in solving practical problems arising from consultancy, Qiwei is more attracted to the pursuing for the simplicity in statistical methodology, as simple methods are often effective, and gain more appreciation in application. With the ever-increasing data size and complexity in this information age, the challenge is to develop simple, if ever possible, statistical methods relevant to solving new and complex practical problems.