A sequential scaled pairwise selection approach to edge detection in nonparanormal graphical models
Bibliographic record
Abstract
Abstract We deal with the problem of edge detection in high‐dimensional nonparanormal graphical models. A nonparanormal graphical model is first transformed into a Gaussian graphical model. Then a sequential scaled pairwise selection (SSPS) method which we propose is applied to the transformed model. The SSPS method is a neighbourhood detection approach which makes use of conditional regression models. First the response vector in each individual conditional regression model is scaled, then the response vectors are pooled together to form a single model. The features in this single model are selected pairwise by a sequential procedure, which reflects the intrinsic symmetry of the edges. At each step of the procedure, the current residual vector is projected into the space spanned by each pair of columns of the design matrix which correspond to the symmetric edges, and the selected set of edges is then augmented by the pair with the largest projection norm. The extended BIC (EBIC) is used as the stopping rule for the sequential procedure. The selection consistency of the SSPS method is established. Simulation studies and the analysis of a real data set are carried out to compare the SSPS method with other existing methods. The simulation studies demonstrate that the SSPS method outperforms the other methods. In addition, the SSPS method is computationally more appealing. The Canadian Journal of Statistics 44: 25–43; 2016 © 2016 Statistical Society of Canada
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".