Subgroup analysis for functional partial linear regression model
Bibliographic record
Abstract
Abstract In a functional partial linear regression (FPLR) model, where the response variable is scalar while the explanatory variables involve both infinite‐dimensional functional predictors and finite‐dimensional scalar covariates, the relationships between the response and the explanatory variables are often assumed to be the same for all subjects. This article relaxes this assumption and considers a subgroup analysis for the FPLR model, which allows the intercepts to vary for different subgroups from a heterogeneous population. By projecting the functional predictors onto the corresponding eigenspace, the subgroup analysis based on the FPLR model can be simplified to a framework that is similar to the classical subgroup analysis problem. To automatically identify subgroups among observations and estimate the regression parameters of interest, we combine the functional principal component analysis with the concave pairwise penalized approach and develop an ADMM algorithm for functional subgroup analysis. We also establish the consistency of the proposed estimators under mild conditions. Simulation experiments demonstrate that the concave penalized subgroup approach could potentially achieve substantial gains over the ordinary FPLR model. The analysis of data from a creative achievement study is used to illustrate the practical performance of the subgroup analysis for the FPLR model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".