publication . Article . Other literature type . Preprint . 2013

Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables

Fellinghauer, Bernd; Bühlmann, Peter; Ryffel, Martin; von Rhein, Michael; Reinhardt, Jan D.;
Open Access
  • Published: 01 Aug 2013 Journal: Computational Statistics & Data Analysis, volume 64, pages 132-152 (issn: 0167-9473, Copyright policy)
  • Publisher: Elsevier BV
Abstract
A conditional independence graph is a concise representation of pairwise conditional independence among many variables. Graphical Random Forests (GRaFo) are a novel method for estimating pairwise conditional independence relationships among mixed-type, i.e. continuous and discrete, variables. The number of edges is a tuning parameter in any graphical model estimator and there is no obvious number that constitutes a good choice. Stability Selection helps choosing this parameter with respect to a bound on the expected number of false positives (error control). The performance of GRaFo is evaluated and compared with various other methods for p = 50, 100, and 200 po...
Subjects
free text keywords: Statistics and Probability, Computational Theory and Mathematics, Applied Mathematics, Computational Mathematics, Random forest, Lasso (statistics), Statistics, Conditional independence, Econometrics, Mixed variables, Error detection and correction, Data set, Mathematics, Graphical model, Interconnection, Statistics - Methodology, Statistics - Applications, Statistics - Computation
Powered by OpenAIRE Open Research Graph
Any information missing or wrong?Report an Issue
publication . Article . Other literature type . Preprint . 2013

Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables

Fellinghauer, Bernd; Bühlmann, Peter; Ryffel, Martin; von Rhein, Michael; Reinhardt, Jan D.;