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Stable Graphical Model Estimation with Random Forests for Discrete, Continuous, and Mixed Variables | | Bernd Fellinghauer
; Peter Bühlmann
; Martin Ryffel
; Michael von Rhein
; Jan D. Reinhardt
; | | Date: |
1 Sep 2011 | | Abstract: | A conditional independence graph is a concise representation of pairwise
conditional independence among many variables. We propose Graphical Random
Forests (GRaFo) 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).
We evaluate and compare the performance of GRaFo with Stable LASSO
(StabLASSO), a LASSO-based alternative, across 5 simulated settings with p=50,
100, and 200 variables, and we apply GRaFo to data from the Swiss Health Survey
in order to evaluate how well we can reproduce the interconnection of
functional health components, personal, and environmental factors, as
hypothesized by the World Health Organization’s International Classification of
Functioning, Disability and Health (ICF).
GRaFo performs well with mixed data and thanks to Stability Selection it
provides an error control mechanism for false positive selection. | | Source: | arXiv, 1109.0152 | | Services: | Forum | Review | PDF | Favorites | |
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