Approximate methods for analyzing semiparametric longitudinal models with nonignorable missing responses
Why this work is in the frame
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Bibliographic record
Abstract
We often encounter missing data in longitudinal studies. When the missingness in longitudinal data is nonignorable, it is necessary to incorporate the missing data mechanism into the observed data likelihood function for a valid statistical inference. In this article, we propose and explore a novel semiparametric approach to estimating the regression parameters and variance components using a partially linear mixed model with nonignorable and nonmonotone missing responses. The finite sample properties of the proposed method are studied using Monte Carlo simulations, where our method is found to be very effective in capturing any curvilinear pattern in the mean response. The method is also illustrated using some actual longitudinal data obtained from a public health survey, referred to as the Health and Retirement Study (HRS).
 Journal of Statistical Research 2022, Vol. 56, No. 2, pp. 155-183
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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 | 0.020 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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