Online updating method with new variables for big data streams
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
For big data arriving in streams, online updating is an important statistical method that breaks the storage barrier and the computational barrier under certain circumstances. In the regression context, online updating algorithms assume that the set of predictor variables does not change, and consequently cannot incorporate new variables that may become available midway through the data stream. A naive approach would be to discard all previous information and start updating with new variables from scratch. We propose a method that utilizes the information from earlier data in the online updating algorithm with bias corrections to improve efficiency. The method is developed for linear models first, and then extended to estimating equations for generalized linear models. Closed-form expressions for the efficiency gain over the naive approach are derived in a particular linear model setting. We compare the performance of our proposed bias-correcting approach and the naive approach in simulation studies with data generated from a normal linear model and a logistic regression model. The method is applied to a study on airline delay, where reasons for delays were only available more recently, starting in 2003.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.004 | 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