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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
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Multilevel network data facilitate statistical inference for curved ERGMs with geometrically weighted terms.
Jonathan Stewart1, Michael Schweinberger1, Michal Bojanowski2
1Department of Statistics, Rice University, 6100 Main St, Houston, TX 77005, USA.
Network Science (Cambridge University Press)
|June 18, 2020
Summary
Multilevel network data improve Exponential Random Graph (ERG) modeling by enabling decay parameter estimation and cross-validation. This enhances model performance and generalizability for network analysis.
Area of Science:
- Social network analysis
- Statistical modeling
- Computational social science
Background:
- Exponential Random Graph (ERG) models are widely used for network analysis.
- Estimating decay parameters in ERG models is challenging with single network data.
- Multilevel network data offer a potential solution to these challenges.
Purpose of the Study:
- To demonstrate the benefits of using multilevel network data for ERG modeling.
- To show how multilevel data facilitate the estimation of decay parameters.
- To assess the out-of-sample performance of ERG models using cross-validation.
Main Methods:
- Utilized a multilevel network sample of classroom networks from Poland.
- Applied ERG modeling techniques to analyze the network data.
- Employed cross-validation to evaluate model performance.
Main Results:
- Estimating decay parameters using multilevel data significantly improved in-sample model performance.
- The best ERG model demonstrated strong out-of-sample performance.
- Findings suggest that the model's results can be generalized to the broader population.
Conclusions:
- Multilevel network data are highly beneficial for ERG modeling.
- The ability to estimate decay parameters enhances model accuracy.
- Cross-validation with multilevel data confirms the generalizability of findings.
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