Doubly Sparse Regularized Regression Incorporating Graphical Structure Among Predictors
Bibliographic record
Abstract
Recent research has demonstrated that information learned from building a graphical model on the predictor set of a regularized linear regression model can be leveraged to improve prediction of a continuous outcome. This thesis proposes the doubly sparse regression incorporating graphical structure among predictors (DSRIG) model, and its logistic regression counterpart, doubly sparse logistic regression incorporating graphical structure among predictors (DSLRIG). In general, the regularization scheme of these models works by building an undirected graph over the predictor set and then using the resulting neighbourhoods of the graph to form a set of (overlapping) groups. Sparsity is encouraged both within and among the groups that contribute to the overall estimation of the regression parameters. Together, DSRIG and DSLRIG provide a unified framework for the fitting of many other commonly used regularization schemes. In this thesis, a combination of simulation and analysis of real world data are used to evaluate and compare model performance. Ultimately, the DSRIG and DSLRIG models improve outcome prediction and parameter estimation compared to previously proposed methods. A finite sample error bound is derived for DSRIG in the case of a quantitative outcome and predictors distributed as multivariate normal. Guidelines for the implementation of DSRIG in the analysis of real world data are also provided.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".