Discrete Choice Estimator Properties for Finite Population and Simulation Sample Sizes
Bibliographic record
Abstract
Econometric models based on simulations are used extensively in transportation. Simulation methods provide only an approximation of the objective function and produce estimators that suffer from bias and loss in efficiency. Two types of bias are known to exist in simulation-based estimators: simulation bias, as a result of the nonlinear transformation in the log likelihood (LL) function, and optimization bias, caused by the maximization operator, which depends on the variance of the simulated LL with respect to the random draws and the population sample. In this paper, the properties of the estimators are studied with resampling techniques in various simulation configurations. In the experiments, optimization bias dominates simulation bias, and in the presence of panel data the use of some randomized quasi–Monte Carlo techniques aiming at reducing simulation variance only marginally affects the estimated parameters for a given sample size. Results also confirm that the population resampling, though numerically costly, is a simple and effective procedure to deliver a better understanding of parameter properties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.075 | 0.396 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".