Inferences in Multinomial Dynamic Mixed Logit Models
Bibliographic record
Abstract
In this paper we propose a general multinomial dynamic mixed logits model which explains how a multinomial/categorical response at a given time can be affected by (1) an individual’s categorical fixed covariates, (2) certain category prone random effects, and (3) an individual’s past multinomial responses. This model may be considered as a generalization of the (a) existing multinomial dynamic fixed models to the mixed model setup with category prone random effects; or (b) existing standard random effects based multinomial dynamic mixed models involving past binary responses to the complete multinomial dynamic (depending on past multinomial) setup, or (c) existing multinomial mixed models to the multinomial longitudinal mixed model setup. We use a conditional fixed effects based likelihood approach for estimation of the parameters. An intensive simulation study is carried out to examine the finite sample performance of the estimators under the general dynamic mixed models, as well as under various specialized models. The proposed model and estimation methodology is also illustrated with a real life longitudinal survey data on health, ageing and retirement in Europe. Asymptotic properties such as consistency of the conditional fixed effects based likelihood estimators of the main fixed effects based regression parameters are studied in details.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".