Bibliographic record
Abstract
This dissertation presents Bayesian choice models that handle choice dynamics and heterogeneity in choice models from a new perspective. In particular, this dissertation proposes three new models in order to (1) capture choice dynamics by estimating time-varying regression coefficients, (2) estimate individual-level nonlinear utility functions, and (3) recover an unknown heterogeneity distribution in a more general way. Chap. 2 aims to demonstrate that an essential element of choice or utility dynamics can be captured by directly modeling changes in the regression coefficients. Consumers' dynamic choice behavior results from a change in their utilities over time, which may be generated by a change in variable importance weights. Toward this end, a Bayesian dynamic logit model is proposed. The proposed model is designed to capture choice dynamics by estimating a vector autoregressive process for the regression coefficients, including intercepts of households' linear utility functions. Chap. 3 presents a new model to capture consumer-level nonlinear utility functions without any parametric assumption on these functions. In particular, heterogeneous consumer-level utility functions are estimated by introducing splines under an additive regression setting. Chap. 4 discusses a more flexible nonparametric mixture model, a Dirichlet process prior with normal mixtures, to capture a heterogeneity distribution.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".