Multicollinearity, autocorrelation, and ridge regression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The presence of multicollinearity can induce large variances in the ordinary Least-squares estimates of repression coefficients. It has been shown that ridge regression can reduce this adverse effect on estimation. The presence of serially correlated error terms can also cause serious estimation problems. Various two-stage methods, have been proposed to obtain good estimates of the regression coefficients in this case. Although the multicollinearity and autocorrelation problems have long been recognized in regression analysis, they are usually dealt with separately. This thesis explores the joint effects of these two conditions on the mean square error properties of the ordinary ridge estimator as well as the ordinary least-squares estimator. We show that ridge regression is doubly advantageous when multicollinearity is accompanied by autocorrelation in both,the errors and the principal components. We then derive a new ridge type estimator that is adjusted for autocorrelation. Finally, using simulation experiments with different degrees of multicollinearity and autocorrelation, we compare the mean square error properties of various estimators.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it