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RaProR - Random Projections for Bayesian linear Regression


[Members] [Support] [R-Package] [Literature]


Members

  • Dr. Leo Geppert
  • Prof. Dr. Katja Ickstadt
  • Dr. Alexander Munteanu
  • Prof. Dr. Christian Sohler
We thank our former student assistants Jens Quedenfeld for the initial implementation and Ludger Sandig for the current version of the R package.


Support

Research supported by Deutsche Forschungsgemeinschaft, SFB 876, project C4.


R-Package

The R-package RaProR can be used to calculate a sketch of a large data set. That is a substitute data set of the same dimension but smaller number of observations. As we show in [1], the sketch can be used to perform approximate Bayesian or frequentist linear regression. More specifically, the likelihood as well as the posterior will be close to the ones obtained on the original data. Any algorithm for the regression analysis will run much faster on the sketch than on the original data set given that its running time depends on the number of observations.

Installation

The current version of the package is available at the CRAN repositories via https://CRAN.R-project.org/package=RaProR and can be installed via the standard procedure described in the R manual installing packages.


Literature

[1] Geppert, LN, Ickstadt, K, Munteanu, A, Quedenfeld, J, Sohler, C: Random projections for Bayesian regression, In Statistics and Computing 27, 79-101 (2017). DOI 10.1007/s11222-015-9608-z.


Last update Feb 07, 2020 by A Munteanu and LN Geppert