Semiparametric regression is an extension of regression that permits incorporation of flexible functional relationships using basis functions, such as splines and wavelets, and penalties and is now well-developed for cross-sectional, longitudinal and spatial data. Almost all semiparametric regression analyses process the data in batch. That is, a data set is fed into a semiparametric regression procedure at some point in time after its collection. This talk discusses doing semiparametric regression in real time, with data processed as it is collected and made immediately available via modern telecommunications technologies. Regression summaries may be thought of as dynamic web-pages or iDevice apps rather than static tables and figures on a piece of paper. Online processing of data is an old idea and has a very large literature. Our work uses Bayesian approaches, that handle automatic choices of smoothing parameters, and make use of fast variational approximations that are amenable to online updating. This talk represents joint research with Dr Jan Luts and Ms Tamara Broderick


Matt Wand

Research Area

University of Technology, Sydney


Fri, 01/11/2013 - 4:00pm to 5:00pm


OMB-145, Old Main Building, UNSW Kensington Campus