A Tutorial on Regularized Partial Correlation Networks
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Summary
This tutorial introduces the reader to estimating the most popular network model for psychological data: the partial correlation network and describes how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data.
- Type
- article
- Published
- 2016-07-05
- Cited by
- 2,510
- References
- 114
- Access
- Open access
- OpenAlex
- https://openalex.org/W2462099493
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:4465562
Keywords
Hyperparameter, Partial correlation, Latent variable, Computer science, Correlation
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- State of the aRt personality research: A tutorial on network analysis of personality data in R
- Maximum likelihood estimation of the polychoric correlation coefficient
- Multiplicative latent factor models for description and prediction of social networks
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- High-dimensional graphs and variable selection with the Lasso
- A new method for constructing networks from binary data
- A practical solution to the pervasive problems ofp values
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