Regularized Gaussian Psychological Networks: Brief Report on the Performance of Extended BIC Model Selection
By Sacha Epskamp. 2016
66 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/6010a1c646f4debb5dbb00c3cdeebd2574cfc609
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Sacha Epskamp
- Eiko I. Fried
- D. Hevey
- Alexander P. Christensen
- Hudson F. Golino
- Denny Borsboom
- G. Costantini
- Lourens J. Waldorp
Citations in the corpus
- Estimating psychological networks and their accuracy: A tutorial paper (2016, unknown): “The GGM requires an estimate of the covariance matrix as input, 4 for which polychoric correlations can also be used in case the data are ordinal (Epskamp, 2016).”
- A Tutorial on Regularized Partial Correlation Networks (2016, methodology): “Simulation studies have shown that LASSO regularized network estimation generally results in a high specificity, while sensitivity and correlation increases with sample size (Epskamp, 2016; van Borkulo et al., 2014; Foygel and Drton, 2010).”
- Network analysis: a brief overview and tutorial (2018, methodology): “The GGM can also be used for ordinal data, wherein the network is based on the polychoric correlations instead of partial correlations (Epskamp, 2018).”
- Investigating the performance of Exploratory Graph analysis and traditional techniques to identify the number of latent factors: a simulation and tutorial (2018, methodology): “It has a tuning parameter (!) that can be chosen in a way to minimize the extended Bayesian information criterion (EBIC; Chen & Chen, 2008), which has been shown to accurately retrieve the true network structure in simulation studies (Epskamp, 2016; Foygel & Drton, 2010).”
- The Gaussian Graphical Model in Cross-Sectional and Time-Series Data (2016, methodology): “Estimating a GGM with the GLASSO algorithm in combination with EBIC model selection has been shown to work well in retrieving the true network structure (Epskamp, 2016; Foygel & Drton, 2010).”
- Stability and variability of personality networks. A tutorial on recent developments in network psychometrics (2017, methodology): “In the following examples, we use tuning parameter selection via EBIC, which is the default option, for consistency with package qgraph's function EBICglasso.”