Bias/Variance Trade-Off in Estimates of a Process Parameter Based on Temporal Data
Why this work is in the frame
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Bibliographic record
Abstract
We recommend an approach to estimate a process performance measure (or parameter) at the present time from a stream of data where the performance may drift slowly over time. It is common practice to estimate current process performance using either present-time data only or including all historical data. When sample sizes by time period are small, an estimate based only on present-time data is imprecise. When the performance changes over time, including historical data in estimation trades more bias for less variability. We propose to regulate the bias/variance trade-off using estimating equations that down-weight past data. We derive approximations for the variance of the estimator and the distribution of a test statistic involving the estimator. The work is motivated by estimation of a customer loyalty measure where realistic data demonstrates the proposed approach.
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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 | 0.005 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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