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Graphical modeling of binary data using the LASSO: a simulation study

Overview of attention for article published in BMC Medical Research Methodology, February 2012
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Title
Graphical modeling of binary data using the LASSO: a simulation study
Published in
BMC Medical Research Methodology, February 2012
DOI 10.1186/1471-2288-12-16
Pubmed ID
Authors

Ralf Strobl, Eva Grill, Ulrich Mansmann

Abstract

Graphical models were identified as a promising new approach to modeling high-dimensional clinical data. They provided a probabilistic tool to display, analyze and visualize the net-like dependence structures by drawing a graph describing the conditional dependencies between the variables. Until now, the main focus of research was on building Gaussian graphical models for continuous multivariate data following a multivariate normal distribution. Satisfactory solutions for binary data were missing. We adapted the method of Meinshausen and Bühlmann to binary data and used the LASSO for logistic regression. Objective of this paper was to examine the performance of the Bolasso to the development of graphical models for high dimensional binary data. We hypothesized that the performance of Bolasso is superior to competing LASSO methods to identify graphical models.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
France 1 2%
Romania 1 2%
Unknown 40 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 26%
Student > Ph. D. Student 6 14%
Professor > Associate Professor 5 12%
Student > Master 5 12%
Other 4 9%
Other 8 19%
Unknown 4 9%
Readers by discipline Count As %
Medicine and Dentistry 8 19%
Computer Science 6 14%
Mathematics 4 9%
Engineering 3 7%
Environmental Science 3 7%
Other 12 28%
Unknown 7 16%