Real-time semiparametric regression

TL;DR

This paper introduces algorithms for real-time semiparametric regression analysis, enabling immediate processing and updating of complex models using streaming data through Bayesian and graphical model frameworks.

Contribution

It presents a novel online updating approach for a broad class of semiparametric models using variational inference techniques.

Findings

Enables fast, real-time updates for complex semiparametric models.

Demonstrates applicability on stock, real estate, and airline data streams.

Provides an accessible online platform for illustrating the methodology.

Abstract

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.