Faster Estimation of a Discrete-Time Proportional Hazards Model with Gamma Frailty
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
Fitting a complementary log-log model that accounts for gamma-distributed unobserved heterogeneity often takes a significant amount of time. This is in part because numerical derivatives are used to approximate the gradient vector and Hessian matrix. The main contribution of this article is the use of Mata and a gf2 evaluator to express the gradient vector and Hessian matrix. Gradient vector expression allows one to use a few different options and postestimation commands. Furthermore, expression of the gradient vector and Hessian matrix increases the speed at which a likelihood function is maximized. In this article, I present a complementary log-log model, show how the gamma distribution has been incorporated, and point out why the gradient vector and Hessian matrix can be expressed. I then discuss the speed at which a maximum is achieved, and I apply sampling weights that require an expression of the gradient vector. I introduce a new command for fitting this model. To demonstrate how this model can be applied, I will examine information on when young males first try marijuana.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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