Imputation and likelihood methods for matrix‐variate logistic regression with response misclassification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Matrix‐variate logistic regression is useful in facilitating the relationship between the binary response and matrix‐variates, which arise commonly from medical imaging research. However, such a model is impaired by the presence of response misclassification. It is imperative to account for the misclassification effects when employing matrix‐variate logistic regression to handle such data. In this article, we develop two inferential methods that account for the misclassification effects. The first method, called an imputation method, has roots in the score function derived from the misclassification‐free context, and replaces the involved response variable with an unbiased pseudo‐response variable, i.e., expressed in terms of the observed surrogate measurement. The second method is to directly derive the likelihood function for the observed response surrogate and then conduct estimation accordingly. Our development is carried out for two settings where misclassification rates are either known or estimated from validation data. The proposed methods are justified both theoretically and empirically. We analyze the Breast Cancer Wisconsin (Prognostic) data with the proposed methods.
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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 | 0.002 | 0.019 |
| 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 it