Divide and conquer for accelerated failure time model with massive time‐to‐event data
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
Abstract Big data present new theoretical and computational challenges as well as tremendous opportunities in many fields. In health care research, we develop a novel divide‐and‐conquer (DAC) approach to deal with massive and right‐censored data under the accelerated failure time model, where the sample size is extraordinarily large and the dimension of predictors is large but smaller than the sample size. Specifically, we construct a penalized loss function by approximating the weighted least squares loss function by combining estimation results without penalization from all subsets. The resulting adaptive LASSO penalized DAC estimator enjoys the oracle property. Simulation studies demonstrate that the proposed DAC procedure performs well and also reduces the computation time with satisfactory performance compared with estimation results using the full data. Our proposed DAC approach is applied to a massive dataset from the Chinese Longitudinal Healthy Longevity Survey.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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