Nonparametric estimation of the shape function in a Gamma process for degradation 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 The author considers estimation under a Gamma process model for degradation data. The setting for degradation data is one in which n independent units, each with a Gamma process with a common shape function and scale parameter, are observed at several possibly different times. Covariates can be incorporated into the model by taking the scale parameter as a function of the covariates. The author proposes using the maximum pseudo‐likelihood method to estimate the unknown parameters. The method requires usage of the Pool Adjacent Violators Algorithm. Asymptotic properties, including consistency, convergence rate and asymptotic distribution, are established. Simulation studies are conducted to validate the method and its application is illustrated by using bridge beams data and carbon‐film resistors data. The Canadian Journal of Statistics 37: 102‐118; 2009 © 2009 Statistical Society of Canada
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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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