Modern Regression Methods that can Substantially Increase Power and Provide a more Accurate Understanding of Associations
Bibliographic record
Abstract
During the last half century hundreds of papers published in statistical journals have documented general conditions where reliance on least squares regression and Pearson's correlation can result in missing even strong associations between variables. Moreover, highly misleading conclusions can be made, even when the sample size is large. There are, in fact, several fundamental concerns related to non-normality, outliers, heteroscedasticity, and curvature that can result in missing a strong association. Simultaneously, a vast array of new methods have been derived for effectively dealing with these concerns. The paper (1) reviews why least squares regression and classic inferential methods can fail, (2) provides an overview of the many modern strategies for dealing with known problems, including some recent advances, and (3) illustrates that modern robust methods can make a practical difference in our understanding of data. Included are some general recommendations regarding how modern methods might be used.
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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 | 0.059 | 0.223 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".