Some new computational methods in high-dimensional statistical learning in biostatistics
Bibliographic record
Abstract
In recent years, biostatisticians have witnessed and contributed to the fast development of large-scale administrative medical databases and electronic health records.The abundance of information provides great opportunities in many (bio)statistical research areas.Important tasks include the accurate prediction of the outcome of interest and the identification of risk factors for the outcomes.However, the rapidly increasing sample size, dimensionality and complexity of today's datasets pose challenges to statistical methodology and computation.Among which, the high-dimensionality has motivated the idea of sparse modelling -that is, to construct a statistical model using a small subset of variables, based on the assumption that only a few variables are associated with the outcome.This can be achieved, in a data-driven manner, by incorporating some sparsity-inducing regularization into the classic statistical modelling techniques such as the least squares estimation and maximum likelihood estimation.Computationally, these estimators are acquired by minimizing a regularized loss function (least squares, negative log likelihood, etc.).The complexity of the data is another major challenge.Examples include correlations between rei A Tweedie Compound Poisson Model in Reproducing Kernel Hilbert Space
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".