Semiparametric Regression for Streaming Data

Funding: 2014: $130,000
2015: $105,000
2016: $140,000

Project Member(s): Wand, M.

Funding or Partner Organisation: Australian Research Council (ARC Discovery Projects)

Start year: 2015

Summary: Semiparametric regression converts large and complex data-sets into interpretable summaries from which sound decisions can be made. This project tackles semiparametric regression analysis of streaming data - where the data are so voluminous that they may not be storable in standard computer memory and therefore need to be processed rapidly on arrival and then discarded. Effective solutions necessitate a rethinking of semiparametric regression and new approaches will be developed. The project will also develop novel theory and methodology for robotics applications. It will allow analysis of streaming and massive data sets that would not be possible using currently available methods, opening up new applications.

Publications:

McLean, MW & Wand, MP 2019, 'Variational Message Passing for Elaborate Response Regression Models', Bayesian Analysis, vol. 14, no. 2, pp. 371-398.
View/Download from: Publisher's site

Nolan, TH & Wand, MP 2017, 'Accurate logistic variational message passing: algebraic and numerical details', Stat, vol. 6, no. 1, pp. 102-112.
View/Download from: Publisher's site

Keywords: Variational approximation,Real-time data analysis,Robotics

FOR Codes: Statistical Theory, Expanding Knowledge in the Mathematical Sciences, Applied Statistics, Expanding Knowledge in Technology, Statistics not elsewhere classified, Statistical theory , Applied statistics , EXPANDING KNOWLEDGE

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.