Real-Time Semiparametric Regression
- Publication Type:
- Journal Article
- Citation:
- Journal of Computational and Graphical Statistics, 2014, 23 (3), pp. 589 - 615
- Issue Date:
- 2014-01-01
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| LutsBroderickWand14.pdf | Published Version | 739.98 kB |
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© 2014 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. We develop algorithms for performing semiparametric regression analysis in real time, with data processed as it is collected and made immediately available via modern telecommunications technologies. Our definition of semiparametric regression is quite broad and includes, as special cases, generalized linear mixed models, generalized additive models, geostatistical models, wavelet nonparametric regression models and their various combinations. Fast updating of regression fits is achieved by couching semiparametric regression into a Bayesian hierarchical model or, equivalently, graphical model framework and employing online mean field variational ideas. An Internet site attached to this article, realtime-semiparametric-regression.net, illustrates the methodology for continually arriving stock market, real estate, and airline data. Flexible real-time analyses based on increasingly ubiquitous streaming data sources stand to benefit. This article has online supplementary material.
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