Regularized cross-sectional network modeling with missing data: A comparison of methods
Bibliographic record
Abstract
Many applications of network modeling involve cross-sectional data of psychologicalvariables (e.g., symptoms for psychological disorders), and analyses are often conductedusing a regularized Gaussian graphical model (GGM) employing a lasso, alsoknown as the graphical lasso or glasso. Appropriate methodology for handling missingdata is underdeveloped while using glasso, precluding the use of planned missingdata designs to reduce participant fatigue. In this research, we compare three approachesto handling missing data with glasso. The first resembles a two-stage estimationapproach—–borrowed from the covariance structure modeling literature—–wherebya saturated covariance matrix among the items is estimated prior to using glasso. Thesecond and third approaches use glasso and the expectation-maximization (EM) algorithmin a single stage and either use EBIC or cross-validation for tuning parameterselection. We compared these approaches in a simulation study with a variety of samplesizes, proportions of missing data, and network saturation. An example with datafrom the Patient Reported Outcomes Measurement Information System is also provided.The EM algorithm with cross-validation performed best, but all methods appeared to beviable strategies under larger samples and with less missing data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".