Numerical Analysis of the Statistical Properties of Uniform Design in Stated Choice Modelling
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Bibliographic record
Abstract
Abstract Stated choice methods have been widely used in transportation studies since 1980s. In recent years, much research attention has been paid to develop optimal or efficient designs for choice experiments, such as the so‐called D‐optimal design, which does not seek for orthogonality as the traditional approach does but aims at minimizing the determinant of the variance–covariance matrix of the parameter estimators. This paper examines the statistical properties of an alternative design method—uniform design, which also does not look for orthogonality but aims at maximizing uniformity—a measure that is closely related to model efficiency. We compare the estimation efficiency and prediction efficiency of uniform design with that of the traditional fractional factorial orthogonal design in stated choice modelling. Monte Carlo experiments are used to generate models, whose parameters vary in scale. The results show that though uniform design uses a lot fewer profiles than orthogonal designs do, its prediction and estimation efficiencies in stated choice modelling are comparable to that of orthogonal design.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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