Nonparametric estimation for streaming data (Jiadong Mao)

In the era of big data, huge volumes of data are continuously collected in time-varying environment (termed streaming data). A model for streaming data should be fast to compute and adaptive to the changing nature of the data. These two objectives are hard to achieve simultaneously. Conventional statistical methods often assume that all data have already been collected and stored in the computer memory. Existing models for streaming data, are mostly proposed by computer scientists and only address the computational challenge. Without knowing their theoretical properties, it is hard to predict when these methods will succeed.

We develop computationally efficient models with theoretical guarantee, with more flexible nonparametric models, and investigate their theoretical properties using infill asymptotics. Computational properties of the models are shown using computer simulations and then applied to some real data examples to show their power for modelling real-world problems.

https://statsoc.org.au/event-3498067